Sensor system of buried seismic array

ABSTRACT

Microseismic mapping using buried arrays with the integration of passive and active seismic surveys provides enhanced microseismic mapping results. The system is initially set up by recording seismic data with the buried array installation while shooting a significant portion of the 3D surface seismic survey. The 3D surface seismic survey provides the following data: shallow 3D VSP data from the buried arrays; P-wave and converted wave data for the area covered by the buried array that benefits from the planned data integration processing effort; and microseismic data and associated analysis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part of U.S. application Ser. No. 13/759,986,filed 5 Feb. 2013, which is incorporated herein by reference in itsentirety and which claims the benefit of U.S. Provisional Appl.61/595,510, filed 6 Feb. 2012, which is also incorporated herein byreference in its entirety.

BACKGROUND OF THE DISCLOSURE

Land-based seismic surveying uses an array of seismic sensors deployedon the earth's surface in an area of interest. One or more seismicsources (e.g., vibrators, dynamite shots, etc.) generate seismic sourcesignals that travel through the earth, reflect at discontinuities andother features of subsurface formations, and travel back toward theearth's surface. The seismic sensors coupled to the earth at the surfacethen detect the reflected source signals, and a recording unit recordsthe detected signals. Processing of the recorded signals can then beused to image the subsurface for analysis.

Land-based seismic surveys usually do not record extraneous informationto assist in the characterization of a shallow earth model. At most,uphole information is usually recorded at shallow shot/dynamite holes,and that information is then used to improve the shallow “statics”model. Overall, this approach is less than ideal and can be improved.

Microseismic monitoring uses an array of seismic sensors deployed in awellbore or on the Earth's surface to detect seismic energy emanatingfrom various seismic events occurring within the subsurface. Processedsignals from the sensors can identify the position of the event in thesubsurface and the time the event took place. In turn, this informationcan be used in a number of applications to determine movement alongfaults in rock layers or formations, movement of fluid in a reservoir,monitoring hydraulic fracturing operations, etc. In the end, analysis ofthe information can be used in well completion and productionoperations.

A typical form of microseismic monitoring uses an array of sensors(i.e., geophones) deployed downhole in an observation well, which ispreferably located close to a well being monitored. For example, FIG. 1shows a system for determining the distribution and orientation ofnatural fractures in a target well 12. A source 11 pumps fluid for ahydraulic fracturing operation or the like in the target well 12, whichextends below the earth's surface 13 into a fluid or hydrocarbonreservoir 14. The applied pressure from the pumped fluid causes movementalong natural fractures in the well 12, producing a microseismic event17. Seismic waves 18 radiate outwardly from the fracture toward anobservation well 21 located within several thousand feet of the targetwell 12.

Multiple sensors (i.e., geophones) 22 deployed in a vertical array inthe observation well 21 detect the waves 18 from the event 17, and adata recording device 24 records the detected signals. Using variousalgorithms, a signal processor 25 then processes the recorded signalsand determines the arrival times of compressional (P) and shear (S)phases of the seismic event 17 to the sensors 22 so the event'shypocenter can be located in the target well 12. See e.g., U.S. Pat. No.5,996,726. As expected, drilling an observation well can be costly, andthe availability of one or more existing wells for use as observationwells within a suitable distance—usually within 1000 m—may be unlikelyin most cases.

Another approach to microseismic monitoring uses an array 10 ofsurface-based sensors (i.e., geophones) 12 as shown in FIG. 2. The array10 can be arranged to monitor a hydraulic fracturing operation in avertical wellbore 15 using a pattern of the seismic sensors 12 above thearea of interest surrounding the wellbore 15. In response tomicroseismic events, the sensors 12 detect signals related to seismicamplitude, and a recording unit 14 records the signals for processing.

The array 10 has a hub and spoke form. The sensors 12 in the arms of thearray 10 can be spaced at tens of meters from one another, and the armscan extend several thousand meters in length. Because the array 10 isarranged at the surface, there is no need for an observation well. Inaddition, the array 10 can be distributed over a large area of interest.

Because a microseismic event is detected at the surface, surface noisecan be rather large compared to the small event downhole. To overcomethe signal weakness compared to noise, the surface array 10 is beamsteered so points of greatest energy in the subsurface can beidentified. To do this, travel time corrections for subsurface targetpoints are calculated, and the trace data of the surface sensors 12 istime shifted. The data for each target point is stacked so a search ofthe energy distributions in the subsurface can then give the locationsof likely microseismic events. In essence then, this technique attemptsto detect events by stacking the seismic data at an arbitrary startingtime t₀ for the event using a velocity model and stacking. See e.g.,U.S. Pat. Publication No. 2011/0286306 to Eisner et al. It should benoted that the stacking procedure using beam steering can fail to detectevents because the polarity of a microseismic event may not be uniformacross the seismic array 10.

Detecting and locating the microseismic event becomes less reliable asnoise increases, and differentiating real events (i.e., fractures, earthshifts, etc.) from false positives becomes more difficult. In fact, thearray 10 of surface sensors 12 can fail to detect microseismic eventscaused by perforations or fracturing operations when there issignificant surface noise. Although the array 10 of sensors 12 canfacilitate imaging the seismic data, the ultimate uncertainty of whethera real microseismic event has been detected makes it difficult to knowthat what is imaged is an actual event and not just a false positive.

An approach to passive seismic surveying is illustrated in FIGS. 3A-3B.In this approach, wellbores 10 are drilled to a selected depth of about100 meters or less and can be drilled deeper when there is very highlevels of surface noise. Vertically-arranged arrays of seismic sensors(i.e., single component or three component geophones) 12 suspended on acable 16 are placed into each wellbore 10, which is then filled. FIG. 3Bshows how the wellbores 10 are arranged in two-dimensions over thesurface.

When a naturally occurring or induced microseismic event 13 occurs inthe subsurface volume, the sensors 12 detect the seismic energy forrecording by a recording unit 14. The signals detected by each sensor 12are recorded for a selected period of time, and a processor processesthe signals to beam steer the response of the sensors 12 to enhancesignal detection and to reduce noise. For example, each array of sensors12 in a wellbore 10 is beam steered along a predetermined direction, andthe beam steered signals from each vertical array of sensors 12 arecombined.

The beam steering is repeated to focus the response of the array to eachpoint in the subsurface to be evaluated for microseismic events. Fromthis, position and time of origin for the microseismic events can beidentified.

The beam steering is performed by adding a time delay to the signalrecording from each sensor 12. In this way, any event that may haveoccurred at a specific time at a specific location would be expected toreach the sensor 12 at that associated delay time. Therefore, the timedelay applied to the signals depends on the geodetic position and depthof each sensor 12. Additionally, the time delay also depends on thespatial distribution of seismic velocity of the formations in thesubsurface, which is determined beforehand by active source reflectionseismic surveying and combined in some cases with acoustic measurementsmade from wellbores penetrating the rock formations to the target depth.See e.g., U.S. Pat. No. 7,663,970 to Duncan et al. and U.S. Pat.Publication No. 2011/0242934 to Thornton et al.

Although the above microseismic approaches may be effective, it will beappreciated that significant variability exists in a subsurfaceformation at all scales, and the variability directly affects what andhow production can be achieved. For example, experience shows thatproduction along a lateral section of a well is not uniform. In fact,any resulting production from a reservoir tends to come from thosestages that have been fractured, which may not even include all of thehydraulic fracture stages. Being able to more fully understand andcharacterize the high spatial variability of a reservoir will always bean ultimate goal in the well completions industry. To that end,microseismic monitoring has the ongoing challenge of detecting andrecording small signals in a high-noise environment, accurately locatingmicroseismic events, and mapping those events over a wide area.

The related art discussed above with reference to FIG. 1 through FIG. 3Bis not necessarily prior art for the purposes of patentability. Therelated art is merely discussed as background with respect to thesubject matter of the present disclosure.

The subject matter of the present disclosure is directed to overcoming,or at least reducing the effects of, one or more of the problems setforth above.

SUMMARY OF THE DISCLOSURE

In one embodiment, a surveying method and system according to thepresent disclosure integrates active and passive surveying of asubsurface volume in either land-based or marine-based applications.Seismic information is gathered by two arrays of sensors during aseismic survey. Information from one of the arrays is used to augmentthe computation of a property (e.g., sub-surface geology, near-surfacevelocity model, etc.) of the subsurface volume determined withinformation from the other array.

In particular, first arrays of first sensors are arranged in a firstarrangement relative to the subsurface volume, and second arrays ofsecond sensors are arranged in a second arrangement relative to thesubsurface volume different from the first arrangement. First seismicdata is collected with the first sensors in response to first seismicenergy, and second seismic data is collected with the second sensors inresponse to second seismic energy, which may be the same as or differentfrom the first seismic energy. The collected first and second seismicdata is combined, and at least one property of the subsurface volume isdetermined from the combined seismic data.

As one example, the first arrays of the first sensors can be surfacearrays of surface sensors arranged in a desired area on the surfaceabove the subsurface volume. By contrast, the second arrays of thesecond sensors can be buried or shallow arrays of buried sensorsdisposed in boreholes in the desired area of the surface or can be patcharrays of surface sensors arranged in dense arrangements in the desiredarea. Seismic data is collected using the surface sensors and the buriedarray sensors in response to seismic energy. In general, the seismicenergy may be induced or generated in the subsurface volume using aseismic source, such as blast charge, vibrator, etc. However, theseismic energy may be naturally occurring seismic activity in which casethe seismic data is collected passively. Either way, the collectedseismic data from both the surface sensors and the buried array sensorsis combined to determine the property of the subsurface volume.

In further processing, the determined property of the subsurface volumecan be dynamically adjusted based on seismic information collected withburied array sensors in response to microseismic events. In general, themicroseismic events can be actively induced or naturally occurring. Forexample, fractures may be induced in a well in the subsurface volume byoperators performing a hydraulic fracturing operation. Any resultingmicroseismic events generated by the operation can be sensed by theburied arrays in the surrounding boreholes and used to adjust theoriginally determined property of the subsurface volume. Alternatively,the microseismic activity may be naturally occurring in the subsurfacevolume.

In another embodiment, a surveying method and system according to thepresent disclosure detects microseismic events using arrays of sensorsarranged relative to a subsurface volume in either land-based ormarine-based applications. Seismic data is collected with the sensors. Afirst moveout is detected in a first of the arrays by analyzing thecollected seismic data in at least one direction for the first array.One or more second moveouts are detected in one or more second of thearrays by analyzing the collected seismic data in the at least onedirection for the one or more second arrays. When the one or more secondmoveouts are determined comparable to the first moveout, an occurrenceof an event in the subsurface volume is declared in response to thedetermination that the first and second moveouts are comparable.

For example, the array of sensors can be arranged in boreholes in thesubsurface volume. The boreholes can be drilled where desired, andarrays of sensors can be affixed on tubulars, which are then disposed inthe boreholes and cemented in place. This is repeated at severallocations in an area of interest in any desired spacing or pattern. Thesensors can be three-component sensors or can be single-componentsensors possibly, but not necessarily, arranged to sense in threecomponent directions.

During use, a given one of the buried arrays of sensors detects aspecified moveout on a single component (e.g., Z-component). The moveoutmay be linear and may require a certain threshold to be reached before adetermination of sufficient moveout is made. Analysis of the sensorresponse for that same buried array then confirms the presence of themoveout on other components (e.g., X- and Y-components). Again, themoveout can be linear and may need to exceed a given threshold.

If moveout is confirmed on the other components, further analysisinterrogates the seismic data of one or more of the other buried arraysfor a similar event. If the one or more other buried arrays exhibitsensor responses indicative of a similar event, then analysis determinesthat a microseismic or another type of event has occurred. Moveout canbe confirmed at any of the other buried arrays even though there is nopositive confirmation of moveout in the other components of the initialburied array.

Finally, the seismic data from the microseismic event can then be usedfor any of the various purposes disclosed herein. In essence, detectingthe event involves determining that a second type of wave exists when afirst type of wave is identified. For example, if a P-wave is detected,the analysis of the system and method looks for an S-wave arrival afterthe P-wave.

The foregoing summary is not intended to summarize each potentialembodiment or every aspect of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for determining distribution and orientationof natural fractures in subterranean structures.

FIG. 2 illustrates seismic sensors distributed over the ground tomonitor a volume of the subsurface.

FIG. 3A illustrates sensor arrays disposed in wellbores.

FIG. 3B illustrates a plan view of the wellbores having sensors arraysas shown in FIG. 3A.

FIGS. 4A and 4B schematically illustrate plan and elevational views ofan integrated seismic acquisition and monitoring system according to thepresent disclosure for a land-based implementation.

FIGS. 4C and 4D schematically illustrate plan and elevational views ofanother land-based system using different sensor array arrangements.

FIGS. 4E-4F schematically illustrate elevational views of integratedseismic acquisition and monitoring systems according to the presentdisclosure for a marine-based implementation.

FIG. 5 illustrates a flowchart of a seismic survey process according tothe present disclosure.

FIG. 6A schematically illustrates a seismic data processing systemaccording to the present disclosure.

FIG. 6B illustrates a workflow according to the present disclosure.

FIG. 7A-7B illustrates plan and elevational view of a portion of theintegrated seismic acquisition and monitoring system relative to atarget wellbore.

FIG. 8A illustrates a flowchart of a seismic survey process according tothe present disclosure.

FIG. 8B illustrates a schematic plan view of a number of buried arraysshowing their polarity and their confidence factors.

FIG. 9A shows a detection process using the disclosed system.

FIGS. 9B-9C show flowcharts for microseismic event identification usingthe disclosed system.

FIG. 10A illustrates interplay of rock properties and fracturetreatment.

FIG. 10B illustrates how the disclosed system can be used to predictzones of higher productivity.

FIG. 11A compares surface data to buried array data.

FIG. 11B shows noise attenuation with depth.

FIG. 11C shows the use of a median filter to separate upgoing anddowngoing energy.

FIG. 11D shows the moment magnitude determined from P-Wave data withassociated error bars from an example buried array installation.

FIG. 11E shows the moment magnitude determination from S-Wave data withassociated error bars from an example buried array installation.

FIGS. 12A-12C show comparisons between a preferred sensor for the buriedreceivers relative to other sensors.

FIG. 13 illustrates analytic modeling with the preferred sensor.

FIG. 14 illustrates detection results with the preferred sensor in aburied array.

FIG. 15A reproduces components of the disclosed system previouslyidentified throughout the present disclosure.

FIG. 15B shows the disclosed system in more detail to discuss gain andnoise levels involved.

DETAILED DESCRIPTION OF THE DISCLOSURE

A. Integrated Land Seismic Acquisition and Monitoring System

1. System Configuration

FIGS. 4A-4B schematically illustrate plan and elevational views of anintegrated seismic acquisition and monitoring system 100 according tothe present disclosure to generate information (e.g., geological,geomechanical, geophysical, etc.) and image earth subsurface structuresof a subsurface volume or formation. Although discussed in the contextof a land-based implementation, the system 100 can be used in a marinesurvey, as detailed later with reference to FIGS. 4E-4F.

The system 100 includes one or more sources 130, surface receivers 120in a first arrangement relative to the formation (i.e., subsurfacevolume), and a recording unit 140 in communication with the surfacereceivers 120. In this first arrangement, the surface receivers 120 arearranged substantially horizontally in one or more arrays 110 relativeto the formation (i.e., laid on the near-surface of the ground andarranged substantially parallel relative to the ground and the formationbelow). Any acceptable deviation (e.g., angle) from horizontal orparallel can be accommodated, as appreciated by one skilled in the art.

The surface receivers 120 can be deployed as autonomous point receiversor deployed in one or more arrays 110 with cables. Either way, thesurface receivers 120 are spaced about the survey area as shown in FIG.4A, and each surface receiver 120 can have one or more sensors 122. Thespacing of the sensors 122 gives a particular resolution and can bedesigned for a given implementation. The sensors 122 measure geophysicalinformation and can include single component or multiple component(i.e., 3-component) sensors for obtaining multi-dimensional energy. Agiven sensor 122 can include an accelerometer, a velocity geophone, afiber optic sensor, a microphone, or the like, and the array 110 of thesensors 122 can use any combination of these.

In addition to the surface arrays 110, the system 100 has a plurality of“buried” arrays 150 located within or outside the area of the seismicsurvey. The buried arrays 150 consist of a plurality of buried receiversor sensors 152 in a second arrangement relative to the formation. Inthis second arrangement, the sensors 152 in a given array 150 arearranged substantially perpendicular relative to the formation (i.e.,arranged substantially vertical relative to the ground and the formationbelow). Any acceptable deviation (e.g., angle) from vertical orperpendicular can be accommodated, as appreciated by one skilled in theart. Although the arrays are described as being buried, the arrays maybe a “shallow array” of sensors in the near-surface. The buried orshallow array can be permanent or can be re-deployable. Each sensor 152can have one or more single or multiple (three) component sensors.Finally, the spacing of the arrays 150 and the sensors 152 gives aparticular resolution and can be designed for a given implementation.

In general, a given buried sensor 152 can use an accelerometer, ageophone, a hydrophone, a fiber optic sensor, a microphone, or othertype of sensor, which can be the same as or different from the sensors122 used with the surface receivers 120. Likewise, a given array 150 ofthe sensors 152 can use any combination of these types of sensors. Asnoted below, each of the sensors 152 may preferably use athree-component geophone having a geophone element and having alow-noise amplifier integrated therein. The buried sensors 152 arearranged vertically in each array 150 in shallow boreholes 154, whichare several tens of meters in depth as described later.

The one or more seismic sources 130 impart acoustic energy into theground. For this land-based implementation, the seismic sources 130 canbe vibrators, although other types of sources can be used. The sensors122 and 152 receive the imparted energy after reflection and refractionat boundaries in subsurface structures, and the sensor data is thencommunicated to the recording unit 140 using wireless technology orother communication techniques. Finally, the formation may have one ormore wellbores 115 of interest either drilled or proposed to be drilledin the formation to a reservoir below.

The one or more seismic sources 130 used for the acquisition of a surveycan be conventional sources, such as vibrators, dynamite shots, or thelike. If more than one source 130 is used, they may generate differentinput energies into the formation, which can produce different types ofseismic energy into the different arrays 110 and 150. For example, onesource 130 for the surface arrays 110 can use a vibrator, while anothersource 130 for the buried arrays 120 can use a dynamite shot. In fact,the sources 130 used can be different and can produce differentbandwidths, or the sources 130 used can be either the same or different,but can be used simultaneously. These and other variations can be used,as will be appreciated with the benefit of the present disclosure.

In general, the overall configuration of the system 100 (i.e., how thearrays 110 and 150 and sources 130 are arranged, spaced from oneanother, etc.) is determined by the target depth, the desired spatialresolution, and other factors. It is noted that the arrangement of thearrays 110 and 150 in two dimensions need not follow a regular patternas displayed in FIG. 4A, but could also be placed in some more randomarrangement; provided that the surface coordinates of the arrays 110 and150 are known with sufficient accuracy.

During a survey, the system 100 uses the two arrangements (i.e.,orientations and spacing) of the arrays 110 and 150 to offer differentresolutions of the formation and to offer different perspectives orfocal points of the formation relative to the same sources 130 ofseismic energy. The surface arrays 110 arranged horizontal to theformation have a different orientation to the seismic energy than theburied arrays 150 arranged vertical to the formation. The differentperspectives or orientations can, thereby, be used to further enhancethe image of the formation and the properties determined, as disclosedherein.

During a survey, for example, the seismic sources 130 “shoot” into theburied arrays 150 and the signals are recorded. When the signals areprocessed by a data processing system 400, the information from theseburied arrays 150 is used to determine the elastic properties of theshallow earth in the survey area. In turn, the determined elasticproperties are used to optimize the processing of the informationrecorded in the surface receivers 120. Moreover, as discussed later, theburied arrays 150 are also used for passive monitoring of microseismicevents in the subsurface formations.

As shown in FIG. 4B, a few of the buried arrays 150 used in the system100 are schematically shown relative to some of the seismic sources 130,the surface receivers 120, and an existing or future wellbore 115. Asnoted previously, the buried arrays 150 have vertically-arrangedreceivers or sensors 152 disposed in shallow boreholes 154 in the areaof interest. Each borehole 154 has a line of several sensors 152disposed vertically therein. These boreholes 154 can be existingboreholes from surrounding wells or may be drilled for this purpose.Although the sensors 152 could be suspended within the borehole 154 in anumber of ways, the sensors 152 are preferably deployed on piping ortubular 156. The boreholes 154 can be filled with a medium that couplesthe sensors 152 to the surrounding rock and that adequately matches theP and S impedance of the surrounding rock. In some implementations, thesensors 152 can be cemented in the borehole 154, although the sensors152 can be hydrophones disposed in fluid filling the borehole 154.

Regardless of the coupling method, the boreholes 154 may have anappropriate depth for about four to seven sensors 152 deployed at aboutevery 20 m. In general, the boreholes 154 can be drilled deeper whenthere are very high levels of surface noise, or they may be drilledshallower if surface noise is less of a problem. Of course, more or lessreceivers 152 may be used, and they may have any suitable spacing,preferably evenly between one another in the borehole 154.

The two different arrangements of the arrays 110 and 150 in FIGS. 4A-4Brelate primarily to orientation of the sensors, although the density(i.e., spatial density involving spacing between sensors and placementrelative to the formation) used on the arrays 110 and 150 could be thesame or different. Other types of different arrangements could be used.For example, FIGS. 4C and 4D schematically illustrate plan andelevational views of another land-based implementation of the disclosedsystem 100 using different sensor array arrangements. Here, the system100 includes second arrays 160 in the form of surface patch arrays ofmultiple sensors 162. In one example, the patch arrays 160 may have itssensors 162 arranged in a 100-m by 100-m matrix with the sensors 162arranged every 10-m. Other configurations can be used. In generalthough, the patch arrays 160 have a denser collection and configurationof the sensors 162 than the surface arrays 110 and may be placed on theground at different locations from one another than the surface arrays.These surface patch arrays 160 can be used in addition to or instead ofthe buried arrays 150 to provide the second array arrangement for thedisclosed system 100.

2. Survey Process

Having an understanding of the survey system 100, discussion now turnsto a seismic survey process 300 according to the present disclosureshown in FIG. 5. The process 300 is discussed in the context of theland-based implementation of FIGS. 4A-4D, but can equally apply to anyother implementations disclosed herein. Broadly, information gathered bythe first arrangement of sensors 152 (and/or surface patch sensors 162)during a seismic survey augments the computation of a property (e.g.,subsurface geology, near-surface velocity model, etc.) of the formationobtained with the second arrangement of sensors 122.

More specifically, to survey the formation, one or more first arrayshaving a plurality of first sensors are arranged in a first arrangementrelative to the formation (Block 302). These include, for example, thesurface sensors 122 arranged in the arrays 110 at the surface in FIGS.4A-4D. These horizontally-arranged sensors 122 can be arranged andplaced in desired areas of interest and at desired spacing.

Additionally, one or more second arrays having a plurality of secondsensors are arranged in a second arrangement relative to the formation(Block 304). These second arrays can include, for example, the buriedarrays 150 of sensors 152 in FIGS. 4A-4B, which can be arranged andplaced in the desired area and at desired spacing using new or existingboreholes 154. Because the buried arrays 150 are vertical, they have adifferent arrangement (orientation) relative to the formation and theseismic energy than the surface arrays 110. Additionally, the buriedarrays 150 can have different spacing and placement than the surfacearrays 110.

In addition to or as an alternative to the buried arrays 150, the secondarrays can include the surface patch arrays 160 of sensors 162 in FIGS.4C-4D. Even though the surface patch arrays 160 are horizontal and havethe same relative orientation as the surface arrays 110 to theformation, the surface patch arrays 160 still have a differentarrangement relative to the formation than the surface arrays 110because they have different density, spacing, and placement than thesurface arrays 110.

First seismic data 142 is collected with the first sensors 122, andsecond seismic data 142 is collected with the second sensors 152/162 forrecording at the recording unit 140 (Block 306). The collection of datais made in response to seismic energy, which can be from one or moreactive sources 130 (e.g., blast charge, a vibrator, an air gun, a watergun, a sparker, an impulsive source, a compressive source, and a shearwave source) or from a passive source (e.g., earthquake, a faultslippage, production from a local wellbore 115, a fracturing operationin the local wellbore 115, a breaking-up of ice, an environmental sourcewith identifiable location). The data 142 for the two arrays 110 and 150can be obtained at the same or different times.

The collected first and second seismic data are then combined using thedata processing system 400 (Block 308), and at least one property of theformation is determined from the combined seismic data (Block 310). Anyof a number of properties can be determined of the formation and caninclude, but are not limited to, a subsurface structure, a near-surface(compressional and shear) velocity model, anisotropy parameters of thesubsurface, acoustic and shear impedance, inelastic parameter, elasticparameter, formation density, brittleness of the formation's reservoir,rigidity, fracture attribute of the reservoir, density of the reservoir,pore pressure of the formation or portion thereof, and the like.

To determine at least one property of the formation from the combinedseismic data, a model can be imaged of the property by constraining themodel determined with the first seismic data by a constraint determinedfrom the second seismic data. The model used in the imaging can be amodel of velocity, shear velocity, compressional velocity, anisotropicparameter, attenuation parameter, etc., and the imaging process can useKirchhoff-based, RTM-based, or wave equation-based techniques.

In one particular embodiment, the imaging can use wave form inversion(WFI) on the first (e.g., surface) seismic data with penalty constraintsfrom the second (e.g., buried, vertical, or denser) seismic data toconstruct earth model parameters, e.g., compressional velocity, shearvelocity, and density, from the wave form information of the seismicdata. In the WFI technique, a property of the subsurface, such ascompressional velocity, is determined by minimizing a first differencebetween (a) the data recorded at one set of arrays (e.g., array 110 ofsurface receivers 120) and (b) the data as modeled at those samereceivers 120 using a current estimate of the subsurface property. Here,this first difference is further minimized simultaneously with anequivalent second difference for the other set of arrays, such as theburied arrays 150 so that the joint difference is minimized between thesets of arrays 110 and 150. Alternatively, the second difference for theother arrays (e.g., the buried arrays 150) can be minimized, and thepredetermined property resulting from that analysis of the buried arrays150 can then be used as a constraint in the update of the model forminimizing the first difference of the first arrays.

In another embodiment, the imaging can generate a near-surface model ofthe formation by constraining a shallow surface wave inversion with thesecond (e.g., buried, vertical, or denser) seismic data. Then, the first(e.g., surface) seismic data is imaged using the generated near-surfacemodel. In this instance, the second data set (i.e., from a buried array150) provides detailed “uphole” information, which can constrain thesurface wave inversion and provide a more detailed and accuratenear-surface model. As will be appreciated, such a near-surface modelcan help produce an accurate image of the subsurface with the first dataset (i.e., from the surface arrays 110). The uphole information providedby the buried arrays 150 is much richer than the conventional upholeshot times, in that the buried array information provides compressionaland shear velocity information, as well as attenuation measurements. Asa result, a statics correction can be avoided during processing and canbe replaced by a more accurate imaging step through the surface layer.

In further processing, seismic data can be collected with the secondsensors 152/162 (and optionally with the surface sensors 122 as well) inresponse to microseismic energy from either passive or activemicroseismic events (Block 312). When this further microseismic data isanalyzed, the analysis can be used to dynamically adjust the previouslydetermined property of the formation by adding an additional constraintto the property determination (Block 314).

In particular, in combining the collected seismic data from the twoperspectives (surface array 110 along with the buried array 150 and/orpatch arrays 160), the system 100 obtains direct information about theformation property (e.g., velocity model, attenuation, etc.) from oneperspective (arrays 150/160) and refines the property with directinformation from the other perspective (arrays 110). For instance, datafrom the second arrays 150/160 can be used to determine a formationproperty (the attenuation, P-wave velocity model, S-wave velocity model,anisotropy, and the like) at the near surface. This information at thenear surface is then used as a constraint on the same formation propertydetermined by the data from the surface arrays 110.

The microseismic events can be from fracturing, intervention, andproduction, or the events can be naturally occurring. For example, inthe initial survey of Blocks 306 to 310, an initial near surfacevelocity model may be determined based on the readings of the surfacesensors 122 and the other sensors 152/162 in response to active sources130, such as vibrators, dynamite shots, etc. Thereafter, operators maydig local wellbores 115 in the area or may operate existing wellbores115 for fracturing, intervention, production, etc. in the reservoir ofthe formation. Activities such as drilling, fracturing, intervention(i.e., fluid or steam injection), production, and other activeoperations can induce microseismic activity in the formation that isdetected by the second sensors (152/162) (and possibly also the surfacesensors 122), and analysis of the microseismic events may be used toimprove the near surface velocity model. The microseismic events neednot be actively induced, however. Instead, the system 100 can monitorpassive events caused by micro-earthquakes, fault slippage, breaking-upof surface ice, environmental noise with identifiable location (e.g.,passage of a cargo train on a local track), etc. in the area ofinterest.

3. Data Processing and Modeling Examples

Given an understanding of the system 100 and the overall survey processused, discussion now turns to some details on the data processing andmodeling performed on the formation data.

As noted in the Background of the present disclosure, land-based seismicsurveys do not use extraneous information to assist in thecharacterization of the shallow earth model and at most recordinformation at shallow shot/dynamite holes to improve the shallow“statics” model. In contrast, the disclosed system 100 uses upholeinformation obtained at each buried array 150 (i.e., at the buriedsensors 152 in that buried array 150) as additional information tocharacterize a shallow earth model of the area of interest.

The information obtained by the buried arrays 150 is of different types,including upcoming compressional (P) wave energy and shear (S) waveenergy reflected from layers in the earth, as well as energy propagatingin the near surface, which are known as surface waves. Having theplurality of buried sensors 152 within the buried arrays 150, the system100 can obtain a detailed velocity profile of the near surface at eachburied array's location. Using the multiple buried arrays 150 andseismic sources 130, the system 100 can generate and correlate thedetailed velocity profile of the near surface across the area of theseismic survey and beyond. Specialty sources 130, such as shear sourcesor high frequency sources, can be used to maximize the near surfaceinformation obtained with these buried arrays 150. For example, with ashear source 130, shear signals can also be readily observed in theburied arrays 150, thereby forming an accurate shear velocity profile ofthe near surface.

Additionally, the buried arrays 150 and seismic sources 130 can besituated at a variety of azimuths in the survey area. The variation inazimuths between buried arrays 150 and seismic sources 130 can then beused to determine the variability of the shallow earth properties as afunction of azimuth, which is known as anisotropy. To obtain thisinformation, the data processing system 400 processes the recordedsignals 142 from the seismic sources 130 into the sensors 122 and 152using standard seismic techniques or by adapting interferometrictechniques. The resulting, refined information acquired by thisprocessing gives a detailed set of deliverables that are much morerefined than the standard information normally available in a land-basedsurvey.

The integration of passive microseismic with active surface seismic canalso further enhance the near-surface understanding and can enhancec-wave/p-wave imaging and associated reservoir characterizationdeliverables. Briefly, having the sensors 152 placed in the boreholes154 in addition to having the sensors 122 placed at the surface, thesystem 100 can measure energy traveling up through the earth to thesurface (with its different responses due to variations in the nearsurface), but the system 100 can also measure near surface attenuationusing the buried sensors 152. As will be appreciated, attenuationchanges with respect to depth and x-y position, and the attenuationdistorts the frequency of the seismic signals being detected. The system100 in its processing uses a Fourier transform to determine thefrequency content of the seismic signals and determine the attenuation(Q) and other deliverable values. In turn, these values can beinterpolated three-dimensionally over the survey area and can be fedback into the surface model used by the system 100 during its processingof the seismic data. The interpolation of the surface model preferablyrelies on weighting to determine aspects of the near-surface attenuationin those areas of the survey in which buried sensors 152 are notpresent.

Using the buried sensors 152, the system 100 can also measurenear-surface shear wave properties. As will be appreciated,compressional (P) waves arrive in the borehole 154 as Z-components, butthe shear (S) waves arrive as X-Y components. In many instances,conventional seismic data can be incomplete because it lacks an accurateshear wave velocity, especially for the near-surface. The buried sensors152, however, can give a measure of the shear wave velocity, which canalso be interpolated three-dimensionally over the survey for those areaslacking buried sensors 152.

Finally, deliverables of the P-wave velocity can also be determined bythe system 100 from the rich set of data available from the surfacesensors 122 and buried sensors 152. Using all of the values of thesedeliverables, the data processing system 400 can process the seismicsignals of the surface sensors 122 and remove or filter out those signalcomponents, events, and the like that are undesirable or extraneous. Inthis way, the seismic signals provided by the buried sensors 152 enhancethe normal surface acquisition with the surface sensors 122 by thesystem 100. These and other data processing results can be obtained withthe disclosed system 100, as detailed below.

4. Marine-Based Implementation

Although the system 100 in FIGS. 4A-4D was directed to a land-basedimplementation, the benefits of the disclosed system 100 can be appliedto a number of marine-based systems. For example, FIG. 4E schematicallyillustrates the integrated seismic acquisition and monitoring system 100for a marine-based implementation having one or more seismic streamers220 and a source 230 towed by a seismic vessel 200 in a body of water,such as the ocean. Used in conjunction with the streamer(s) 220 and thesource 230, the system 100 has buried arrays 250 of vertically-arrangedsensors 252 disposed in boreholes 254 in the seabed.

The sensors 222 on the streamers 220 can be hydrophones asconventionally used in a marine-based implementation, and the source 230can use one or more air guns, water guns, or other typical marine-basedsource. As noted above, the buried arrays 250 can provide thesubstantially vertical seismic sensing for the disclosed system 100. Fortheir part, the sensors 252 in the buried arrays 250 can beaccelerometers, geophones, hydrophones, fiber optic sensors,microphones, or the like disposed in the boreholes 254 in a mannersimilar to the land-based implementation disclosed previously.

In FIG. 4F, the system 100 for the marine-based implementation again hasthe source 230 towed by the seismic vessel 200 in a body of water. Atthe seabed, the system 100 has buried arrays 250 in boreholes 254 asbefore, but includes ocean-bottom cables 260 with surface sensors 262disposed on the seabed. As an addition or alternative to the buriedarrays 250, the system 100 can have sensors 272 disposed on a verticallyextending line 270. Although the line 270 can be any substantiallyvertical cable extending vertically through the water column, the line270 shown here is actually the tether of the ocean-bottom cable 260. Inthis case, the line 270 extends up from the ocean-bottom cable 260 atthe seabed, through the water column, and to a surface buoy 274 or to avessel. The sensors 272 disposed on this line 270 can, therefore,provide the vertical sensing arrangement for the disclosed system 100.

The marine-based implementations of the system 100 in FIGS. 4E-4F canalso include an existing or proposed well (not shown) as before. Othersources 230 of input energy for seismic surveying can be used than theair gun array depicted. In fact, the vessel 200 may be used in icywaters, where breaking up of ice by the vessel 200 or another icebreaker (not shown) at the water's surface can generate energy forseismic surveying. Marine animal activities, boat operations, etc. canact as sources for surveying. Active seismic sources can be used on theseabed, and operations in a wellbore in the seabed can produce energyfor seismic surveying.

Any of the various components disclosed above with reference to FIGS.4A-4F can be interchanged with one another to arrange first sensors122/222/262 in a first arrangement relative to the formation and arrangesecond sensors 152/162/252/272 in a second arrangement relative to theformation. Thus, references to surface or horizontal sensors and toburied, vertical, or borehole sensors is merely meant to beillustrative. The two arrays of sensors can operate with their differentorientations as disclosed herein and can operate in a manner similar tothe other implementations disclosed herein. In general, the source130/230 can include one or more of a blast charge, a vibrator, an airgun, a water gun, a sparker, an impulsive source, a compressive source,a shear wave source, and the like.

5. Seismic Data Processing System

As noted previously and as schematically illustrated in FIG. 6A, aseismic data processing system 400 can be used to process seismic dataaccording to the present disclosure. In general, the system 400 can useany suitable hardware and software available to store and processseismic data obtained with the data acquisition system 100, such asdisclosed herein. As schematically shown, the seismic data processingsystem 400 has a data input module 403, which obtains seismic data fromthe acquisition system 100. The input module 403 links to variousseismic processes 410 through 436 of the system 400 and ultimately linksto a data output module 404. The processing system 400 may be capable ofprocessing data from a variety of seismic data formats, such as SEGY orSEG-2, and can also convert between different formats by reading oneinput format and writing to a different output format.

The seismic data processing system 400 includes a number of knownfunctions and utilities 410 for processing seismic data, such as tracemerging, bandpass filter, notch filter, debiasing, despike, traceintegration, trace normalization, trace rotation, scaling, sorting,stacking, trace tapers, vibroseis sweep calculations, waveletcalculation, travel time generation, and velocity modeling, among otherpossible functions and utilities which are not discussed in detail here.

Other than some of the conventional functions and utilities 410, thedata processing system 400 can include an attenuation estimator 420.This estimator 420 delivers characteristics of attenuation of theformation by calculating an effective attenuation estimate (Q). Thecalculation estimates attenuation Q for two input traces by (1)correcting the traces using angular and distance corrections, (2)plotting Fourier Transforms of the two corrected traces, (3) plotting alog of the amplitude ratio between the two traces, and (4) calculatingan attenuation estimate Q by calculating a linear regression of the logof the amplitude ratio. The attenuation estimate Q can also involve atwo-layer attenuation estimate. Here, values for thickness, attenuation,and velocity of a layer, along with the effective attenuation Q are usedto determine an attenuation value of a second layer.

Event picking algorithms 422, such as the STA/LTA or Modified EnergyRatio (MER) algorithm, can be used to identify seismic events on aseismic trace. The algorithms 422 can either return the strongest eventidentified on the trace (first break picking) or can return all eventsfound on the trace, along with a numerical score that indicates thestrength of the event.

The system 400 can also include a moment magnitude estimator 424 todeliver a moment magnitude estimate from a recorded seismic event oninput trace data. The estimator 424 applies various correction factorsto the seismic data.

The data processing system 400 can include a microseismic imager 430that generates images of seismic data using diffraction stacking.Various options are available for dealing with normal seismic data(where the T=0 time is known), and microseismic data (where the starttime of the event is unknown). The microseismic imager 430 uses traveltime maps and uses velocity models defined with Z-component specified aseither depth or elevation. Input data is flattened using a ray-tracedtravel time from a given source location to each sensor position. Theflattened data is then processed using one of many amplitudeconditioning steps; such as amplitude (sum all amplitude values alongconstant time), absolute amplitude (the sum of the absolute value ofamplitudes along constant time), squared amplitude (the sumamplitude-squared values along constant time), positive squaredamplitude (the sum of only positive amplitudes and squared), andnegative squared amplitude (the sum of only negative amplitudes andsquared).

The microseismic imager 430 generates a one-dimensional array of datafor each shot location; the length of the array is equal to the recordtrace. The result from an amplitude conditioning step is passed to aseismic imaging step that does one of the following: computes the sum ofall amplitudes, chooses the maximum value, or chooses the minimum value.In turn, this value is placed at the shot X, Y, and Z location in theoutput image space, and the algorithm repeats for the next shot X, Y,and Z location.

In addition to the above described imaging algorithm, the imager 430 cancalculate the semblance or mean covariance to preferentially weight datathat looks “flat”, while de-emphasizing data that does not look “flat”.This may result in significantly cleaner images than what can beaccomplished using standard diffraction stacking. The data processingsystem 400 can include a microseismic modeler 432 that generatessimulated microseismic events by ray tracing through a velocity modeland convolving the ray-traced travel times with a wavelet. Anycombination of surface and subsurface geometries can be generated tosimulate arrivals from surface arrays (110), buried arrays (150), andmonitor wellbores. If the modeler 432 calculates kinematics (traveltimes) and not dynamics (amplitudes), then microseismic events such asdouble-couples may be suitably handled by adding an amplitudemodification for the desired source mechanism.

The data processing system 400 can include a microseismic database 434that contains a collection of application entities that model variousmicroseismic-related data objects. This design allows an application towork with these objects, while the storage and retrieval of objects isperformed via a database.

Finally, the data processing system 400 can include a microseismicapplication 436, which can be an end-user microseismic processingsoftware application. The application 436 includes event detection,event location, microseismic imaging, moment magnitude calculation,moment tensor inversion, and various display tools to help an end userinterpret seismic data.

6. Workflow

To optimize the position of the survey, a preferred workflow 450 in FIG.6B is used so the survey can be positioned in a promising area of ageological play, such as shale or unconventional play or evenmarine-based environments. The workflow 450 also describes how to extendand use the information to obtain geological, geophysical, andgeomechanical properties from the integrated acquisition and monitoringsystem 100.

As shown in FIG. 6B, conventional geological evaluation 452 andpetrophysical evaluation 454 can be done to define and characterize ageological play in which the integrated acquisition and monitoringsystem 100 can be used. These can be used to analyze rock physicsattributes 456, such as acoustic impedance, shear impedance, Poisson'sratio, geomechanics, brittleness, etc.

Once the integrated acquisition and monitoring system 100 is used tocharacterize the reservoir, the system 100 can provide a number ofdeliverables 458 based on processing and analysis of the seismic data,including P-wave inversion; joint inversion of P-wave and converted-wavedata; shear-wave splitting; anisotropy parameters of the subsurface;acoustic and shear impedance; elastic parameters; inelastic parameters,formation density; various maps of reservoir attribute for brittleness,rigidity, fracture, and density, as well as others; attenuation; porepressure, etc. The deliverables thereby enable operators to develop astrategy 460 for completing and producing the play. Finally, asdiscussed in another section of the present disclosure, the system 100can also be used in determining the effectiveness of completionsoperations 462 by passive monitoring with the buried arrays 150 tomeasure microseismic events, as will be discussed below in Section B.

B. Passive Monitoring Using Buried Arrays

In the previous discussion of the disclosed system 100, the buriedarrays 150 (and/or patch arrays 160) have been integrated with thesurface arrays 110 to enhance the land-based or marine-based seismicacquisition and analysis. In addition to this approach, the arrays150/160 in the survey area near a target wellbore 115 can be used forpassive monitoring of microseismic events that are either naturallyoccurring or induced by fracture, perforation, intervention, orproduction operations in a well, as hinted to previously. Preferably,the type of array used is the vertically-arranged buried arrays 150 dueto their particular orientation relative to the formation.

FIGS. 7A-7B show just a portion of the system 100 discussed previously,omitting the surface receivers (120) and the like. Although shown for aland-based implementation, the same discussion applies to a marine-basedimplementation, as in FIGS. 4E-4F. As before, the vertical or buriedarrays 150 consist of a plurality of single or multi-component receiversor sensors 152 arranged vertically in shallow boreholes 154 several tensof meters in depth. The sensors 152 can be strapped or attached to apipe 156 disposed in the borehole 154 and cemented in place. Sections ofPVC pipe coupled by collars can work well for this purpose.

Rather than using seismic sources (130) at the surface as in the activeacquisition discussed previously, the system 100 uses the passive sourceof a microseismic event 117 occurring in or near a target wellbore 115for seismic surveying. The microseismic event 117 may be induced by afracture, perforation, or intervention operation; by production offluids from the formation; by injection of fluids into the well; or bysome other operation.

The sensors 152 detect the seismic energy generated by the event 117,and the recording unit 140 records the sensor signals for laterprocessing. In turn, the recorded signals from the detected event 117are processed by the data processing system 400 to determine theproperties of the microseismic event 117 that produced the signals.

The system 100 having the buried arrays 150 can be used to also recordnaturally occurring events, such as caused by a micro-earthquake andfault slippage, in the subsurface not related to any drilling,intervention, or production activities. Thus, the analysis describedbelow can also be applied to these naturally occurring events, and in sodoing, can establish a baseline of activities prior to drilling,intervention, and production related activities. After collecting thisbaseline information, a comparison with microseismic activity generatedafter drilling, intervention, and production activities are commencedcan enhance the property determinations of the formation and can give ameasure of the safety of the drilling operations, as well as a measureof the potential impact of these operations on shallow aquifers or othergeological structures of interest.

The system 100 can, therefore, be used for on-demand monitoring to moreaccurately locate hypocenters of microseismic events 117 by using theenhanced near-surface model obtained from the integrated buried arrays150 and surface seismic data of the previous discussion. For mappingusing microseismic events 117 during a fracture treatment, for example,existing data is used to construct initial velocity, anisotropy,statics, and attenuation model(s). The integrated system 100 asdiscussed in the previous section is then used to record 3D data intothe buried arrays 150 so the initial model of velocity, anisotropy,statics, and attenuation can be updated. Also, as disclosed earlier, thepresent system 100 can be used when no activity is occurring to record abaseline of microseismic activity, with the same benefit provided by therefined earth model.

Having the updated model, operators then perform the fracture treatment,perforation operation, or other intervention. For example, operators maypump treatment fluid down the wellbore 115 with a surface pump 113 andfracture a portion of the formation, or a perforation may be made in thecasing of the wellbore 115. Meanwhile, the passive monitoring of thesystem 100 conducts continuous recording of seismic signals. The data ofthe seismic signals obtained with the buried arrays 150 is delivered infield to recording units 140 and eventually to the data processingsystem 400, where data conditioning and clean-up can be performed.

Through processing with the techniques detailed herein, the dataprocessing system 400 detects microseismic events 117 and locates thehypocenters of those events 117. A number of calculations are thenperformed to display and analyze the events 117. For example, the momentmagnitude and location of the hypocenter are calculated with erroranalysis, and the hypocenters can be displayed in a map view,cross-sectional view, 3D view, histogram, cross-plot, etc. so thehypocenters can be used for advanced imaging. Moment tensor inversionanalysis is performed for the hypocenter corresponding to each event117, and a fault fracture network can eventually be constructed from theinformation. Additionally, after the microseismic events 117 are located(or in conjunction with that activity), the full moment tensor of theseismic event 117 can be obtained by inverting not only the times ofarrivals of the microseismic events 117 at the buried arrays 150, but byinverting the phases and polarities of the events 117 as well. Thismoment tensor can then be interpreted to characterize the ways in whichrocks actually broke and to generate from this information networks offractures within the earth.

When sensing microseismic events 117, a primary difficulty isdetermining whether an event has actually occurred. To that end, amicroseismic monitoring process 500 shown in FIG. 8A can be used toanalyze seismic energy and determine with a confidence factor orprobability level whether a microseismic event 117 has been detected. Inthe seismic surveying of the formation, the arrays 150 of sensors 152are arranged substantially vertical relative to the formation as notedpreviously, and data of events is collected with the sensors 152. Theseismic data for the vertical arrays 150 is then analyzed (Block 502).As noted herein, the event 117 may be actively or passively induced.

A moveout at a first array (e.g., 150A; FIG. 7A) is detected byanalyzing the collected data for the first array 150A (Block 504). Asused herein, moveout can refer to relative arrival times of seismicenergy at the sensors 152 in relation to an offset between the sensors152 in a given component direction (e.g., Z direction). Moveout canrefer to the change in frequency of the seismic energy at the sensors152 in relation to the offset between the sensors 152 in a givencomponent direction. Yet still, moveout can refer to the change inamplitude of the seismic energy at the sensors 152 in relation to theoffset in the given direction. Thus, in addition to relative arrivaltimes, moveout can refer to frequency change (attenuation) and/oramplitude change (decay) of the seismic energy's signal in relation tothe sensor offset.

Detection of the moveout at the array 150A looks for a particularprogression of the seismic energy detected across the sensors 152 of thearray 150A. For example, the moveout may exhibit a particular linearprogression in accordance with how the sensors 152 are verticallyarranged and how the seismic energy of a microseismic event 117 in theformation would propagate across, and be detected by, the array 150A ofsensors 152. In other words, the relative time differences betweensensor detections at the array 150A can be linear, although othercharacteristics such as a quadratic relationship of the distances may besensed between sensors 152. Details related to the linear-style moveoutat an array are shown in the traces of FIG. 14.

Either way, the moveout for the array 150A may need to have a particularcharacter (i.e., linear slope, polarity, duration, function, etc.) andmay need to exceed some desired threshold level in order to be adetected moveout of interest. When a moveout of interest is detected, aconfidence factor or probability level indicating detection of amicroseismic event 117 is increased (Block 506). This confidence factorcan be used in later processing to determine that a microseismic event117 has actually been detected.

As further confirmation, the seismic data of the array 150A can beanalyzed to detect that a second type of wave is detected at a later orearlier point in time after a first type of wave has already beenidentified. For example, analysis may indicate that moveout from aP-wave is detected at the array 150A at a point in time. Furtheranalysis can then look for the moveout from the arrival of a comparableS-wave after the identified P-wave. This analysis can be performed onthe same component direction (e.g., Z) of the same array 150A or can beperformed on one of the other component directions (e.g., X or Y) of thesame array 150A.

Thus, after detecting the first moveout in at least one componentdirection of the first array 150A, the process 500 may analyze theseismic data of the other component directions of the sensors 152 in thefirst array 150A to detect the same moveout in the other componentdirections (e.g., X- and/or Y-components) (Decision 508). If the firstmoveout is detected in one or both of these directions, then theconfidence factor can be increased (Block 510).

After detecting the first moveout at least in the first array 150A, asecond moveout is detected in one or more second ones of the arrays150B-C by analyzing the collected data for the one or more second arrays150B-C at a comparable or expected time that the purported event 117would be detected at the one or more second arrays 150B-C (Block 512).

Again, the second moveout can be detected in one component direction(e.g., Z-component) (Decision 514) to increase the confidence factor(Block 516) and can be detected in the other component directions (X-and/or Y-components) (Decision 520) to even further increase theconfidence factor (Block 520). Details of the moveouts from the arrays150A-C are compared to determine that the moveouts are comparable to oneanother (Decision 522). These steps 514-522 can be repeated for severalof the vertical arrays 150A-C of the system 100.

To be comparable, two moveouts at different sensor arrays 150 occuraround the same expected time and have the same characteristics. Forexample, the moveout detected by the sensors 152 at one array 150A mayexhibit a particular linear progression in accordance with how thesensors 152 are vertically arranged and how the seismic energy of amicroseismic event 117 in the formation would propagate across, and bedetected by, the array 150A of sensors 152. In other words, the relativetime differences between sensor detections at the array 150A can belinear, although other characteristics such as a quadratic relationshipof the distances may be sensed between sensors 152. Either way, themoveout then for the second array 150B to be comparable may have thesame character (i.e., linear slope, duration, function, etc.) at acomparable or expected time. The polarity of the moveouts between thearrays 150B-C, however, may be different, with one array 150A showingmovement (pushing or pulling) in one direction opposite to any of theother arrays 150B or 150C.

In any event, the system 100 can declare an occurrence of a microseismicevent 117 in response to the determination that the moveouts detected byseparate arrays 150A-C are comparable (Block 524). For instance, thevalue of the confidence factor can be used in this declaration and candefine a quality of the confidence of a microseismic event's occurrenceand detection. Such a confidence factor can be determined at each array150A-C and summed to make a final determination.

As can be seen above, building the confidence factor that a microseismicevent 117 has been detected may or may not depend on looking at moveouton several component directions of the sensors 150A-C. Although this canincrease the confidence factor, it is not strictly necessary that thesame moveout be detected in the other component directions at the samevertical array 150A-C because not all seismic energy may be detectablein this manner. Instead, the process 500 can continue the analysis ofother arrays 150B-C even though the process 500 does not detect the samemoveout at the same array 150A in different component directions atBlock 508.

Over the area of interest of a formation, final confidence factors canbe associated with the events 117 and the arrays 150A-C. When processingthe seismic information, weights based on the confidence factors can beapplied to the various events 117 at the buried arrays 150A-C whenhandling the information to adjust any model, property, or other aspectdetermined for the formation. For instance, FIG. 8B shows a schematicplan view of a number of buried arrays 150A, B, C, etc. The polarity (+or −) of each array 150A-C is indicated along with a confidence factorthat the array 150 has detected moveout from a microseismic event 117.

Although shown in very simplistic terms in FIG. 8B, the expanse of themicroseismic information with its polarity and confidence levels acrossthe underlying formation (below the arrays 150) can help furthercharacterize the microseismic event 117, the formation below, and howthe seismic information from the microseismic event 117 can beintegrated or combined with the surface seismic data from the surfacearrays (not shown). In a general sense, the confidence factors can beused as weighting factors when using the seismic data of the buriedarrays 150 as constraints to the surface seismic data whencharacterizing or modeling properties of the underlying formation, asdisclosed herein.

As noted above, detecting and imaging microseismic events 117 hasseveral challenges. Primarily, there are several unknown variablesassociated with a microseismic event 117 that occurs naturally (e.g.,micro-quakes, earth shifts, etc.) or that is even induced by fracturingor other operation in the wellbore 115. In particular, the starting timet_(o) for the event 117 is unknown, and even the mechanism acting as thesource of the event may not be known. Further, detecting microseismicevents 117 has to deal with very weak seismic signals and with very highlevels of noise in comparison to those weak signals. Additionally, thedetection has to deal with how velocity, attenuation, anisotropy, andother properties affect the weak seismic signal. Moreover, attempts atstacking seismic signals can destroy the resulting image if detailsrelated to the source mechanism are not incorporated.

Expanding on the process of FIG. 8A, FIGS. 9A-9C show some additionaldetails for detecting microseismic events 117 and handling thechallenges involved. One of the gateway challenges involves the abilityof the disclosed system 100 to initially detect a microseismic event 117even though the source mechanism and starting time of the event 117 arenot known when seismic signals are detected by the buried sensors 152.To that end, the passive monitoring of microseismic events 1147 by thedisclosed system 100 follows several levels of detection 620 as outlinedin FIG. 9A.

In a first level (622), each of the given buried arrays 150 detectsseismic signals, and the system 100 determines that a microseismic event117 has occurred by first looking at the detected moveout—i.e., how theevent 117 has been detected by the plurality of sensors 152 for each ofthe given arrays 150. To do this, the system 100 takes a given array150A for analysis. Because the sensors 152 for the given array 150A areat the same general location, there will not be polarity flips at thegiven array 150A. Therefore, the buried sensors 152 of the given array150A detect the moveout of the microseismic event 117 with linearsemblance (or equivalent event detection techniques, such as tau-ptransform) in which the Z-component of the lower most receiver 152detects the seismic signal, the next sensor 152 detects the signal inthe Z-component a time after, and so on up the array 150A. Thus, thesystem 100 determines that a potential microseismic event 117 has beendetected by the given array 150A if the detection of the seismic signalpasses up in the Z-component along the receivers 152 of the array 150linearly. The detection may look at the moveout as it related tofrequency changes and attenuation changes along the vertically arrangedsensors 160. Finally, the detection may also require a threshold signalvalue to eliminate detection of signals caused by various anomalies,false positives, or noise.

At the same given array 150A, the system 100 can then look for the samevelocity of the detected event in the horizontal components of theburied sensors 152 of the given array 150A. The velocity of the detectedevent is determined by the slope of the seismic detection in theZ-component of the sensors 152. In the horizontal components, this sameslope can be found in the seismic detection of the sensors 152. Theslope of that detection event is very nearly the local “apparent”velocity of the compressional wave at the location of the array 150A inthe subsurface.

A similar procedure can be done to find compression-type events in thehorizontal components of the recorded data, with the associated slopebeing the local “apparent” P-wave velocity as well. Similarly, theprocedure can be used for S-wave detection of events at the same buriedarray 150A by locating coincident events in the horizontal and verticalcomponents, with slopes approximately equal to the apparent shear wavevelocity of the subsurface at the buried array location. The eventdetections at the same buried array 150A are then used as a robustnessindicator or confidence factor as noted above, which is associated withthe detected event.

As noted above, the velocity at which the event arrives at the borehole154 is an “apparent” velocity—not necessarily the true P- or S-wavevelocity of the near-surface in the region of the borehole 154. Theapparent velocity is equal to or greater than the true P- or S-wavevelocity at the borehole 154. The apparent velocity can be greater thanthe true velocity at the borehole 154 because the event 117 can bearriving at an oblique angle to the array 150. The apparent velocity canexactly match the P- or S-wave velocity at the borehole 154 if the eventis directly under the array 150.

Briefly as an example, FIG. 14 shows in the vertical component column,the traces of an array (150) of buried sensors (152) detecting acompressional wave moveout 590. A comparable shear wave is then expectedto follow detection of the compressional wave 590 so that analysis looksfor a moveout from the shear wave on the same vertical component thathas a similar slope and arrives at the array after an expected delaybased on the existing velocity model. In fact, FIG. 14 shows detectionof a comparable shear wave moveout 595 in the vertical direction by thesensors in the array after such an appropriate time.

Additionally, the horizontal (North) component of the buried array'ssensors (152) detects a moveout at a same time comparable to thecompressional wave moveout 590 and detects another moveout at a sametime comparable to the shear wave moveout 595. Thus, detection of onetype of wave in one or more component directions of the array (150) canbe used to track and locate possible detection of other types of wavesin other component directions. This can also be repeated between thevarious arrays (150) of the system 100 by accounting for relativedifferences in velocity and position.

Returning to FIG. 9A, if detection of a potential microseismic event 117has been triggered at the given array 150A, the system 100 proceeds to asecond detection level (624). Here, the system 100 determines whetherthere is any coincident detection of the event 117 at the buried arrays150B-C at different surface locations. Finding coincident detection inother arrays 150B-C uses a particular time window based on the physicalarrangement of buried arrays 150A-C and the ground model.

Performing this detection level, the system 100 can determine whethertwo or more buried arrays 150A-C have detected the microseismic event117 under the first level (622) of detection. If not, then the detectionby the one given array 150 can be regarded as noise or false positive.Otherwise, the detected seismic signals at the two or more arrays 150A-Cgives further indication that the signals result from a microseismicevent 117. The polarity or phase of the event 117 need not be the sameat each of the buried arrays 150A-C. Therefore, the polarity of theevent 117 at each buried array 150A-C is detected and recorded forfuture determination of the moment tensor. This feature of the system100 can eliminate some of the difficulties noted for the related artdiscussed in the Background.

At the third level (624), the system 100 determines coincident detectedsignals for the microseismic event 117 in both P- and S-waves at eachburied array location, as well as the polarity and phase of the event117, and then determines the coincidence across the plurality of buriedarrays 150A-C. If both coincident P-waves and S-waves have been detectedby multiple buried arrays 150A-C, the system 100 can have some certaintythat the event 117 detected is a microseismic event in the seismic data.If only coincident P-waves or S-waves have been detected, the system 100can have less certainty about the detection. This level of certainty istranslated in the present system 100 as a robustness indicator orconfidence factor for the detected event 117.

Once the event 117 is detected and determined to be a microseismicevent, the system 100 uses any variety of beam steering algorithms andmethods (e.g., Kirchhoff methods or wave-equation methods, such asReverse Time Migration (RTM) techniques) to find the hypocenter of themicroseismic event. In the final level (628), the system 100 usesvarious equations disclosed herein to determine properties of themicroseismic event 117 for analysis. Processing of at least some of thedetected signals discussed herein can be handled in real-time.Otherwise, post-processing activity using recorded data can be performedas will be appreciated.

Also, the system 100 images the event 117 from the seismic signalsobtained with the buried sensors 152. The detection scheme (620) doesnot have to deal with polarity variations with azimuths depending on thesource mechanism. In fact, by using the plurality of seismic signals ofthe buried arrays 150A-C locating the event 117, the source mechanismcan be reconstructed.

FIG. 9B shows a scheme (630) for monitoring microseismic events 117 inthe field. The system 100 is set up by having the buried arrays 150A-Cinstalled in the field (632) and orientation shots are recorded (634).Seismic data from the orientation shots is sorted by absolute time ofrecording (i.e., converted to shot gathers) (636), and the raw data isprocessed to remove noise (638). The orientation shots are used todetermine the sensor orientations and to orient horizontal and verticalsensors 152 of the buried arrays 150A-C (640). Raw data is processed toremove noise (642), and an optimum processing flow is determined (644)for handling event data as discussed below.

With the initial setup completed, the microseismic monitoring isperformed in the field (650). As the input data arrives from the fieldduring a fracture operation or the like (652), the seismic data issorted by absolute time of recording (i.e., converted to shot gathers)(654). Based on orientation angles determined earlier in stage (640),the collected data is corrected for sensor orientations (656), and thepreviously-determined optimum processing flow is applied to themicroseismic data (658).

From the corrected seismic data, the system 100 performs event detection(660) (see FIG. 11B). After detecting the microseismic events 117, thesystem 100 determines the hypocenters for the detected microseismicevents 117 (680) and calculates moment magnitudes for each hypocenter(682). The system 100 can use any variety of beam steering algorithmsand methods, e.g., Kirchhoff methods or wave-equation methods, to findthe hypocenter of the microseismic event. Depending on the sourcemechanism of the event, the amplitudes may be peaks on some boreholesand troughs on other boreholes. Beam steering based on simply summingthe event amplitudes together may result in a weak and inaccurate image.Modifying the imager to correct for amplitude variations related to thesources mechanism can provide significantly improved images.

Knowing the hypocenters and moment magnitudes, the system 10 performsmoment tensor inversion for each hypocenter (684) and determines thefault fracture network from the moment tensors (686). Algorithms andmethods disclosed herein are used for these calculations anddeterminations.

FIG. 9C shows further details of the event detection (660). For eachburied array 150A-C (662), prospective events can be picked on a singletrace of the buried array 150A-C using a Short Term Averaging/Long TermAveraging (STA/LTA) algorithm (664), which is a standard method foridentifying valid events and picking arrival times in microseisms causedby hydraulic fracturing. Prospective events can also be picked on asingle trace of the buried array 150A-C using a Modified Energy Ratio(MER) algorithm, which can give consistent first-arrival times on noisymicroseismic traces, or by using any other appropriate technique.Preferably, the event detection uses more than one trace or an entireset of traces of a buried array to determine the moveout of the event,as discussed herein, for example, with reference to FIG. 14.

Semblance is calculated on all traces of the buried array 150A-C todetermine the apparent velocity of arriving events (666). Adetermination may be made whether a prospective event has beenidentified on an acceptable number of receivers 152 on the arrays 150A-C(668) so the system 100 may calculate the apparent velocity of the eventbased on arrival times (670). A determination is then made whether theapparent velocity is within acceptable limits (672). If so, the system100 passes the picked times for the event and the seismic traces to theevent location (674), which is detailed in stage (680) of FIG. 9C. Thisevent detection flow (660) can be performed on either vertical orhorizontal components and can be used to search for either P- or S-waveevents.

As noted previously, confidence factors and polarity can be determinedfor the event 117. When a number of events have been detected, they canbe imaged by converting polarity of the events 117 to the same polarity.For example, all events 117 can be converted to the same polarity byconverting + polarity to + and converting − polarity to +. With thepolarities the same, each of the converted events 117 is then weightedwith its corresponding confidence factor determined during analysis. Theweighted events 117 are then summed together when imaging the events117. In addition to weighting, the events 117 can be scaled orexponentiated. As noted above, the confidence factor used for theweighting can be based on semblance, covariance, coherence, or othersimilarity measure of the moveouts of a given event 117 detected at agiven array 150.

Imaging the events can use compressional waves only, shear waves only,or both compressional and shear waves simultaneously. When compressionaland shear waves are imaged, any mismatch in the imaging of the events117 between the two wave types can be used to update the property of theformation used in the imaging process. This updating can use eitheriterative techniques or waveform inversion algorithms.

Various imaging techniques can be used to image the events. For example,imaging the events 117 can use waveform inversion in which components ofan objective function of the wave form inversion are weighted based onthe previously determined confidence factor. Additionally, imaging theevents can use elastic imaging in a RTM sense.

FIG. 10A conceptually shows some results from the passive monitoring ofmicroseismic events. Plots of microseismic event hypocenters 550 areshown having the determined ductile property of the rock; the latter isdetermined by inversion of surface seismic data for surface sensorsprocessed with or without the benefit of the buried information. Thesehypocenters 550 are plotted in relation to the wellbores 560 and theseismic map 570 in the background. As will be appreciated, theinformation provided by the microseismic events can reveal details ofthe interplay of rock properties and fracture treatment. Additionally,the microseismic events can be used to update the velocity model,especially in the near vicinity of the microseismic event location. Thisupdated velocity model can in turn be used to improve surface seismicimaging or improve positioning of other near-by microseismic events.

As shown in FIG. 10B, the results of passive monitoring and mappingusing microseismic events can assist in predicting zones of higherproductivity and help optimize completion strategies. Two sections ofwells 560A and 560B are shown with information about the rock propertiesdetermined by monitoring the microseismic events occurring during afracture treatment. Different stages can be graphed relative to treatingpressure to indicate those stages having rock with more or less abilityto crack under treatment. Again, such information can predict zones ofhigher productivity and optimize completion strategies.

As can be seen in FIG. 11A, a comparison of surface data to buried arraydata indicates that the buried sensors are best able to detectmicroseismic events, such as that indicated at 580.

As indicated by FIG. 11B, when a buried sensor (152) is used below 20-m,the system 100 can show improved S/N ratios because there can besignificant attenuation of the surface noise with respect to depth.Therefore, the depth of the buried sensors 152 is preferably at leastbelow about 20 m. For example, in one implementation, four buriedsensors 152 on an array 150 can be installed at depths of 100 m, 80 m,60 m, and 40 m from the surface. As will be appreciated, FIG. 10B isonly indicative of the resultant decrease in surface noise with depthfor a specific place on the earth surface. In general, the level ofnoise improvement with depth will vary with shallow surface conditionsso that the level of noise improvement is generated within each buriedlocation to optimize the depth placement of the sensors 152 within theburied arrays 150.

Once the arrays 150 are installed in the boreholes 154, orientationshots can be used to determine the horizontal orientation of thereceivers' sensor orientations. These shots can be the same seismicsources that are fired into the surface seismic arrays (120; FIG. 4A)and the buried arrays 150 as previously described, or they can bededicated sources used purely for that purpose. Also, as can be seen inFIG. 11C, a median filter can be used to separate upgoing and downgoingenergy in the seismic signals detected with the sensors 152 so thatproper energy can be analyzed. Other filtering techniques can also beused to achieve wave mode separation. These filtering techniques aredesigned to remove artifacts from the seismic traces so that the momentmagnitude and the moment tensors can be determined from these tracesgenerated by the microseismic events.

FIGS. 11D and 11E show examples of the moment magnitudes determined fromP-wave microseismic events and shear-wave microseismic eventsrespectively. The P-wave and shear-wave events are also illustrated inFIG. 14 described later. In FIG. 11D, for example, the moment magnitudesdetermined from P-Wave data are shown with associated error bars from anexample buried array installation in which various string andperforation shots have simulated microseismic events. In FIG. 11E, themoment magnitudes determined from S-wave data are shown with associatederror bars from the example buried array installation in which variousstring and perforation shots have simulated microseismic events.

C. Sensor Technology for Receivers in Buried Arrays

As noted previously, surface noise is another challenge to detectingmicroseismic events with the buried arrays. As analysis has alsodetermined, the ability of the buried receivers to properly record weaksignals from the microseismic events depends on the sensor technologyand associated noise floor, the gain settings and associated noise floorof the recording unit, and the system's susceptibility to contaminationfrom environmental electromagnetic noise. Therefore, consideration ofthe sensor technology of the buried receivers 152 and the pairingbetween the receivers 152 and the recording unit can be necessary forproper recording of weak (small) microseismic signals.

In fact, observations directly indicate that the combination of varioussensors and recording systems can provide surprising results. A numberof sensors (e.g., geophones) are available in the art for use as buriedreceivers 152. For example, some available sensors for the buriedreceivers 152 include the SM-64, the SM-6 Normal Sensitivity, the SM-6High Sensitivity, and the VectorSeis (also referred to as SVSM)—each ofwhich is available from INOVA Geophysical Equipment Limited. However,the SM-64 sensor has been identified as a preferred sensor type for useas the buried receivers 52. Other sensors are prone to undesirablenoise, while the SM-64 sensor reduces the effects of the above-describedissues. The SM-64 sensor is a high-sensitivity 3C analog geophone thathas an amplifier with a low-noise chip integrated with the geophoneelement. The amplified signal from the SM-64 sensor is sent to arecording system, overcoming gain settings and associated noise floorshortcomings. Particular details of such a sensor are disclosed in U.S.Pat. No. 7,518,954 to Hagedoorn, which is incorporated herein byreference in its entirety. As detailed herein, a preferred sensor forthe buried receivers is the SM-64 sensor or comparable sensor having anamplifier with a low-noise chip integrated into the geophone element ofthe sensor.

FIG. 12A compares seismic data of sensors and recording systemsresponding to two string shots in a target well. Seismic data of anSM-64 sensor in a buried array 150 coupled a recording system is shownin the third column. This data is shown relative to the seismic data ofother sensors in the buried array 150 showing in the first, second, andfourth columns. These other sensors include the SM-6 Normal, SM-6 HighSensitivity, and SVSM. As can be seen, the seismic data of the SM-64sensor coupled to the recording system has less noise, making it bestsuited for monitoring microseismic events. It is noted that the onlyelastic events present (i.e., events generated in the subsurface by themicroseismic source as well as any elastic noise events generated in thesubsurface) are the ones recorded by the SM-64 sensor. All other noisesappearing on the other geophones as well as on the SM-64 sensor aretherefore events associated with the electronic noise generated by thecombination of the sensor and the associated surface recordingequipment. Therefore, although a surface set of equipment can be chosento optimize another sensor such as the SM-6 sensor, care is preferablyexercised that in so doing one does not enhance natural elastic noises,as can be appreciated.

Further to the above point, FIG. 12B compares the effective noise floorfor several combinations of the SM-64 sensor and recording systems. Bycontrast, FIG. 12C compares the effective noise floor for severalcombinations of the SM-6 sensor and recording system. As can be seen,the ability of a receiver sensor to properly record weak signals ishighly dependent on gain settings of the recording system it is coupledto. Yet, using the SM-64 sensor for the buried receivers 52 can reducethe effects of this issue.

FIG. 13 shows analytic modeling of far-field maximum velocity (m/s)relative to calculated moment magnitude M_(w). The SM-64 sensor has goodresponse from −4 dB to −1 dB, and the noise floor of the SM-64 sensor isdepicted. The moment magnitude M_(w) range for conventional seismicevents is depicted next to the moment magnitude M_(w) range formicroseismic events. The low noise floor of the SM-64 sensor makes itwell suited for detecting P-wave and S-wave energy without undesirablenoise.

FIG. 14 shows seismic data using SM-64 sensor technology in the buriedreceivers 152 to detect a microseismic event—imitated here as aperforation shot. By overcoming inherent noise floor limitations foundin conventional sensors and systems, the SM-64 sensor technology in theburied receivers 152 can detect P-wave and Shear-wave events. FIG. 14also shows how the Z component of the receivers (right panel) can detectthe event linearly as the event's P-wave travels through the subsurfacealong the buried array 150. These P-wave detections can be found in thedetection process discussed previously with respect to FIGS. 8A and9A-9C as S-wave detection in the horizontal components (i.e., horizontaleast in first panel and horizontal north in second panel).

7. Calculations and Deliverables

As noted above, the system's processing can provide a number ofcalculations and deliverables. Some of these are discussed below in moredetail.

a. Moment Magnitude and Moment Tensor

For the purposes of calculating microseismic events or any other seismicevent, displacement for a P or S wave is given by the known equation:

${u\left( {x,t} \right)} \approx {\frac{M_{0}*A_{F}}{4\pi\;\rho\; c^{3}r}*{\overset{.}{g}\left( {t - {r/c}} \right)}}$where c=P or S:

u(x,t) is the displacement in m;

A_(F) is an angular factor;

M₀ is the Moment in Nm;

ρ is the density in kg/m³ (2500 for example following);

c is the P or S wave velocity in m/s (P=2500, S=1250);

r is distance from event to surface in m (r=3000 m);

g(t) is the source function (dimensionless).

In a microseismic event as in an earthquake, strain is releasedgenerating Frictional Energy E_(f) and breaks the rock, which will slipwith energy E_(G) and will radiate energy in the form of seismic wavesE_(R). At the sensors then, the system 100 measures the radiated energyE_(R) which is given by:E _(R) =Fac*4πρcr ²∫_(−∞) ^(∞) {dot over (u)}(t)² dtwhere Fac is a number depending on the wave type—i.e., ⅘ for shear wavesand 2/15 for P-waves due to the angular factor.

The released strain can be related to an equivalent charge of dynamiteor other seismic source. For example, the equivalency between energy ofa dynamite charge to moment magnitude M_(w) is given by:log E _(TNT)=1.5M _(W)+6.66

Quality control tools of the data processing system (400) can assesswhether an entire fracture network has been recorded. Event detection isoften biased by distance as weak events can only be detected by sensorsclose to the source. Therefore, analysis by the system 400 needs tocompensate for this bias, or results could be misleading. Modeling canbe performed to determine optimum placement of sensors.

The seismic moment is defined as:U _(n) =M _(pq) *G _(np,q)where U_(n) is the component of the total displacement in the Cartesiandirection n; M_(pq) is the moment tensor; and G_(np,q) is the derivativeof the Green's function. The * denotes a convolution in the time domain.The Einstein convention summation over repeated indices is assumed. TheGreen's function can be easily calculated knowing the Earth model, andthe displacement is measured, from which the full moment tensor can becalculated by an inversion process.

The magnitude of the moment tensor is the Moment M₀. The moment M₀ iscalculated as follows in a constant earth medium:

$M_{O} = {\frac{4\pi\;\rho\; c^{3}r\; U}{F} = {\mu\;{dA}}}$where M_(O)=seismic moment (Nm);

${c = {{velocity}\left( \frac{m}{s} \right)}};$r=distance from source to receiver (m); U=displacement (m); F=sourcemechanism angular factors; μ=shear modulus; d=average faultdisplacement; and A=area of fault.

The Kanamori Moment Magnitude M_(W) is calculated as:M _(w)=⅔(log₁₀ M _(O)−9.1)

Moment magnitudes can be calculated for P-waves or S-waves and can becalculated on every component (vertical, east, and north). Momentmagnitude estimation is very dependent on data conditioning. Ifconditioning is not carefully done, the system 400 can calculate momentmagnitude of noise. If a filter is applied to remove the noise, theamplitude of the very weak signals of the microseismic event isdecreased, which in turn causes moment magnitude to be negativelyaffected. This results in lower moment magnitudes than reality, butshows better separation between events of different source strength.

b. Attenuation Estimation

The system's processing can provide an estimate of the attenuation. Asis known, inelasticity and inhomogeneities dampen a signal as it travelsthrough a medium. To estimate the size of a seismic event with thesystem 100, the amount of energy that has been absorbed needs to bedetermined. Accordingly, an objective of the system's processing is todetermine a quality factor, Q, as a measure of the attenuation. Thiscalculation also includes multiple layers if necessary.

In general, the energy lost can be written as:

$\frac{2\;\pi}{Q} = \frac{\Delta\; E}{E}$where ΔE is the energy lost in one cycle, and Q is the quality factor.

From this definition of Q, the amplitude of a measured signal is relatedto the true (original signal) by:A _(i)(f)=exp(−πft _(i) /Q)A _(t)(f)S _(i) /R _(i)where

A_(i) (f)=measured spectral amplitude at location i (known);

A_(t) (f)=true spectral amplitude at source (unknown);

S_(i)=the angular correction factor at location i (known), whichaccounts for the radiation pattern;

t_(i)=travel time to location i from the source (known);

f=frequency (known), after Fourier transform of signal;

Q=quality factor (unknown); and

R_(i)=distance to sensor i from the source (known), which accounts forthe geometrical spreading.

To determine the quality factor Q, the observed spectrum is taken at twoor more sites, and the signals are compared:A _(i)(f)=exp(−πft _(i) /Q)A _(t)(f)S _(i) /R _(i)A _(j)(f)=exp(−πft _(j) /Q)A _(t)(f)S _(j) /R _(j)

Division yields:

$\frac{A_{i}(f)}{A_{j}(f)} = {{\exp\left( {- \frac{\pi\;{f\left( {t_{i} - t_{j}} \right)}}{Q}} \right)}\left( \frac{R_{j}}{R_{i}} \right)\left( \frac{S_{i}}{S_{j}} \right)}$

Taking the Log, yields:

${{Log}\left( \frac{A_{i}(f)}{A_{j}(f)} \right)} = {\left( {- \frac{\pi\;{f\left( {t_{1} - t_{2}} \right)}}{Q}} \right) + {{Log}\left( \frac{R_{j}S_{i}}{R_{i}S_{j}} \right)}}$

The right hand side can be fit to get the slope as a function offrequency to get the attenuation Q.

The value of attenuation Q is very sensitive to the range of frequenciesused. Plotting attenuation Q as a function of frequency helps in theselection of the best range of frequencies. Uncertainties arise from theinhomogeneity of the medium as values for the attenuation Q can varydrastically in different layers of the subsurface. An effectiveattenuation value is readily calculated from the attenuation Q of eachlayer.

The data processing system (400) can use software code (e.g., programmedin C++, Java, MATLAB, etc.) to determine the attenuation values forperforation and string shots. The software code can calculate thesurface layer attenuation Q given its thickness, velocity model, and thevalue of the base attenuation Q.

c. Moment Magnitude Estimation

The system's processing can provide an estimate of the moment magnitudeas a measure of a size of an event. Knowing the moment magnitude canhelp describe the events being viewed. The moment magnitude of an eventis determined from the received signal, and the value of the momentmagnitude is intended to be consistent with the values determined fromother receivers.

The spectral amplitude at a given receiver P_(R), is related to thespectral amplitude at the source by the equation:

${P_{R}(f)} = {\frac{A_{SR}}{r}{\exp\left( \frac{{- \pi}\;{ft}}{Q_{eff}} \right)}{T(f)}{P_{S}(f)}}$

There are four corrections involved, including distance, angle, receiverresponse function, and attenuation. T(f) refers to the receiver responsefunction and includes sensitivity. The variable r refers to the sourcereceiver distance, and A_(SR) is the angular correction for thesource-receiver orientation. Additionally, t is the travel time from thesource to the receiver. In the above equation, t/Q_(eff)=t₁/Q₁+t₂/Q₂,where the subscript 1 refers to the surface layer and 2 refers to theremaining material.

P_(S) is the “true” spectral amplitude at the source. The functionP_(S)(f) can be inverse Fourier transformed to give a velocity record atthe source (u_(S)′(t)) as a function of time. This is the timederivative of the displacement, i.e.:u _(S)′(t)=Inv(P _(S)(f))

The time derivative of the moment rate M_(o)″(t) can be determinedthrough the velocity record by:M _(o)″(t)=4πρ_(eff)α_(eff) ³ u _(S)′(t)where ρ_(eff)=is the effective density, and α_(eff)=the effective P-wavevelocity.

Through integration, the moment rate can be determined, which can thenbe used to calculate the moment magnitude using:M _(m)=(⅔)log₁₀(M _(o)*1×10⁷)−10.7where the 1×10⁷ is to convert to ergs from Joules.

The data processing system (400) can use software code to perform thecalculations. When applied to the procedure of perforation and stringshot data, the results of the software code can be consistent among thereceived signals from multiple receivers and comparable to estimatedvales from known sources.

d. Window Tapering Functions

The system's processing can provide tapering functions to taper databeing windowed so the data preferably goes smoothly to zero at theboundaries of the window. This helps to eliminate spurious oscillationsassociated with the Gibbs' phenomenon of overshooting when performingFourier transforms and other artificial effects. Therefore, the dataprocessing system (400) can use software code of tapering functions toadjust windowed data so that the windowed data goes to zero smoothly atthe boundary. The software can have a catalog of routines that can beused to taper the data. For example, the software can include thefollowing taper functions: Bartlett, Blackman, Cosine, Gaussian,Hamming, Hann, Kaiser, Lancos, Rectangular, Triangular, and Tukey.

The time windows used in processing the seismic data is uniformlycentered on the arrival pulse and limited in the range to the signalbeing studied. The data within the windows is tapered to avoid spuriousoscillations as a function of window size (e.g. Hamming filter).Stacking the data from nearby receivers improves signal-to-noise ratio.

e. Semblance Calculation

The system's processing can provide semblance as a measure of thesimilarity between signals. Similar to cross-correlation, which measuressimilarity by examining the sum of products of seismic amplitudes,semblance measures trace similarity by comparing the energy of the sumof trace amplitudes to the sum of trace energies.

The system's processing can determine the similarity of signals so thatsimilar events can be grouped and so that noise can be distinguishedfrom events in picking routines. The system's processing can also findthe lag times between signals that give maximum semblance, which canrefine the arrival times of the signals. Finally, the system'sprocessing can create an interface so that the time differences found inthe semblance routines can be used in locator programs.

For computational purposes, the semblance is the energy of the sum ofthe trace values divided by the sum of the energy of the traces. It hasa maximum value of 1. The semblance for M traces can be written as:

$S = \frac{\sum\limits_{i = 1}^{N}\left\lbrack {\sum\limits_{j = 1}^{M}{x_{j}\left( t_{i} \right)}} \right\rbrack^{2}}{M{\sum\limits_{i}^{N}{\sum\limits_{j}^{M}{x_{j}^{2}\left( t_{i} \right)}}}}$

Interest lies in finding the semblance between pairs of traces and bymaximizing the semblance determining the corrected lag time. In thiscase, the semblance can be rewritten as:

${S(\tau)} = \frac{\sum\limits_{i}\left( {{f\left( t_{i} \right)} + {g\left( {t_{i} + \tau} \right)}} \right)^{2}}{2{\sum\limits_{i}\left( {{f\left( t_{i} \right)}^{2} + {g\left( {t_{i} + \tau} \right)}^{2}} \right)}}$

Here, τ the lag time, t_(i) is the time samples in the windowed trace,and f & g are the two traces. By varying the lag time, the maximumsemblance can be found. The maximum semblance preferably gives the bestoverlap and allows corrections to be made to the pick times. Note thatif f and g are the same trace, τ=0 for the maximum semblance and S(0)=1.

Therefore, the data processing system (400) can use software code thattakes events picked from a seismic data file and determines the lag timefor maximum semblance when compared with a reference trace. The lagtimes can then accurately represent the shift in receiving times.

f. Hypocenter Location Routines

The system's processing can calculate the location (hypocenter) ofmicroseismic events using the robust network of receivers. An accuratelocation allows for mapping of the reservoir, determination of theattenuation, and determination of moment magnitude and moment tensors.Therefore, the data processing system (400) can have software routinesthat take data from multiple sensors and is able in real-time toidentify the hypocenter location of the event. Two routines can be usedto calculate the hypocenter locations.

i. Grid Search

A first routine uses a grid search to calculate the hypocenterlocations. The routine uses time differences as inputs because absolutetimes are unnecessary. A grid of sites is developed with calculatedtravel times from each node to each sensor. Each observed timedifference is compared to the grid to find potential grid sites. Thosepotential sites are then searched sequentially for the nextreceiver-primary difference.

All matches of station differences 1 & 2 are found, and then searchesare made for matches 1 and 3, then 1 and 4. Each search has fewerpossibilities. This process continues until only one site remains. Avariable precision of acceptability can be used that increases if nosites are found that match the criterion.

Therefore, the data processing system (400) can have software code thatapplies this procedure to locate hypocenters of the microseismic events.Using a grid of size 100×100×100, the system 400 can locate a hypocenterin less than 2 seconds.

ii. Non-Linear Least Squares Search

A second routine uses a non-linear least squares search to calculate thehypocenters of the microseismic events. In general, the non-linear leastsquares (NLLSQ) search finds a hypocenter that minimizes the square ofthe difference between the observed arrival times and the calculatedarrival times from that hypocenter to the receivers.

In the procedure, T(S_(i),R) is a function that gives arrival timesdepending on the station locations (S_(i)) and the event location(R=(x,y,z,t)). This creates a grid of times. If t_(i) are the observedarrival times, location R is desired, which minimizes (where the sum isover all stations):

$\sum\limits_{i}\left( {t_{i} - {T\left( {S_{i},R} \right)}} \right)^{2}$

Differentiating this expression with respect to the location R=(x,y,z,t)and linearizing results in:

${tdiff}_{i} = {{\sum\limits_{x,y,z,t}{\frac{\partial{T\left( {S_{i},R} \right)}}{\partial R}{dR}}} \equiv {{\nabla_{R}{T\left( {S_{i},R} \right)}} \cdot {dR}}}$where tdiff_(i) = t_(i) − T(S_(i), R)

The derivative is a function of the position of the event and theposition of the receivers. This gives a linearized estimate of thechanges in dR needed to reduce t_(diff) to 0. In Matrix notation, thisis given by:

${\begin{pmatrix}\frac{\partial T_{1}}{\partial x} & \frac{\partial T_{1}}{\partial y} & \frac{\partial T_{1}}{\partial z} & 1 \\\frac{\partial T_{2}}{\partial x} & \frac{\partial T_{2}}{\partial y} & \frac{\partial T_{2}}{\partial z} & 1 \\\frac{\partial T_{3}}{\partial x} & \frac{\partial T_{3}}{\partial y} & \frac{\partial T_{3}}{\partial z} & 1 \\\frac{\partial T_{4}}{\partial x} & \frac{\partial T_{4}}{\partial y} & \frac{\partial T_{4}}{\partial z} & 1\end{pmatrix}\begin{pmatrix}{\Delta\; x} \\{\Delta\; y} \\{\Delta\; z} \\{\Delta\; t}\end{pmatrix}} = \begin{pmatrix}{tdiff}_{1} \\{tdiff}_{2} \\{tdiff}_{3} \\{tdiff}_{4}\end{pmatrix}$

Using matrix pseudo inversion yields:

$\begin{pmatrix}{\Delta\; x} \\{\Delta\; y} \\{\Delta\; z} \\{\Delta\; t}\end{pmatrix} = {\begin{pmatrix}\frac{\partial T_{1}}{\partial x} & \frac{\partial T_{1}}{\partial y} & \frac{\partial T_{1}}{\partial z} & 1 \\\frac{\partial T_{2}}{\partial x} & \frac{\partial T_{2}}{\partial y} & \frac{\partial T_{2}}{\partial z} & 1 \\\frac{\partial T_{3}}{\partial x} & \frac{\partial T_{3}}{\partial y} & \frac{\partial T_{3}}{\partial z} & 1 \\\frac{\partial T_{4}}{\partial x} & \frac{\partial T_{4}}{\partial y} & \frac{\partial T_{4}}{\partial z} & 1\end{pmatrix}^{- 1}\begin{pmatrix}{tdiff}_{1} \\{tdiff}_{2} \\{tdiff}_{3} \\{tdiff}_{4}\end{pmatrix}}$

In general, it is desirable to dampen this change (multiply by a numberless than 1) of position to keep it in the grid; but it is not desirableto overshoot the minimum. (The shift in the position gives a directionto move but not necessarily a magnitude.) The shift in position is addedto the estimated position and a new position is determined. The processis repeated until the shift in position is less than a grid step.

To that end, the data processing system (400) can have software codethat codes this formalism for the same grid as before. Typically, fourto ten iterations are required, and a variable damping parameter is usedto keep locations within the grid. Calculation times for this procedurecan be several times faster than for grid search, and faster convergencecan be obtained with more receivers.

g. Double-Difference Calculation

The system's processing can use a double difference calculation todetermine error. There are several sources of error that limit theaccuracy of location algorithms. For instance, arrival time measurementscan create errors depending on whether the arrival times are chosenmanually or numerically. There are also limitations from sampling rates,noise, and consistent identification of the arrival. In addition, theremay be errors in the model of the velocity structure. Inherent in themethods described is the velocity of the phases. These can be estimatedfrom a variety of sources, but are only approximately known.Furthermore, inhomogeneities can affect results.

Details related to the sensor network can generate errors. For instance,the placement of receivers plays a role in the location of events.Preferably, the sensor are well spaced “around” the event. Finally,identifying the phase of a wave being “picked” can be difficult, butknowing the phase can be useful because different phases have differentwave speeds.

Through the use of semblance (cross-correlation) techniques and the useof a double-difference formulation, the first two sources of errors(i.e., arrival time measurements and velocity model) can be reduced. Ingeneral, arrival times are either manually or automatically picked. Ineither situation, errors in individual arrival time measurementscontribute significantly to the errors in locating the hypocenter of anevent. The measurement error can be reduced if differences in arrivaltimes are employed (rather than absolute times) using time-domainsemblance and/or cross spectra techniques. This is done by convertingthe arrival times into time differences between common phases ofdifferent earthquakes received at the same station. Arrival times for aset of events considered simultaneously can better constrain therelative locations between events through a double-difference routine.Because the phases of two nearby quakes traverse similar paths, theirtravel time difference will not be significantly affected by model errorin the velocity structure. These two techniques reduce the error fromarrival and from velocity structure model error.

To that end, the data processing system (400) can have software thatuses cross-correlation and double-difference to locate microseismsarising in geothermal systems (e.g., in Basel, Switzerland and inKrafla, Iceland). The software can employ double-difference techniquesto locate events. This code can use the locations found above asstarting points and can then relocate them to improve accuracy.

The double-difference technique uses the difference of observed andcalculated arrival times that are then differenced for separate events.The arrival time is written as the origin time plus the travel time:t=Σ∫uds=τ+T

Here, τ is the time of origin of the event, and u (=1/v) is the slownessthat is integrated over a path. T is defined as the travel time. Sincethe travel time T has a nonlinear dependence on the event location, atruncated Taylor series is commonly used to linearize the equation:

${{\Delta\;{m \cdot \frac{\partial T}{\partial m}}} + {\Delta\;{m \cdot \frac{\partial\tau}{\partial m}}}} = {\Delta\;{m \cdot \frac{\partial t}{\partial m}}}$

Here, m is a vector describing the event location (x,y,z,τ) and origintime, ≢m=(Δx,Δy,Δz,Δτ). The difference between the observed and thetheoretical (calculated) arrival time for an event i received at stationk is given by:

$r_{k}^{i} = {\left( {t^{obs} - t^{cal}} \right)_{k}^{i} = \begin{matrix}{r_{k}^{i} = {{\Delta\;{m^{i} \cdot \frac{\partial\tau}{\partial m}}} + {\Delta\;{m^{i} \cdot \frac{\partial T}{\partial m}}}}} \\{= {{\Delta\;{\tau^{i} \cdot \frac{\partial\tau}{\partial\tau}}} + \left( {{\Delta\;{x^{i} \cdot \frac{\partial T_{k}^{i}}{\partial x}}} + {\Delta\;{y^{i} \cdot \frac{\partial T_{k}^{i}}{\partial y}}} + {\Delta\;{z^{i} \cdot \frac{\partial T_{k}^{i}}{\partial z}}}} \right)}} \\{\equiv {\left( \frac{\partial t_{k}^{i}}{\partial m} \right) \cdot \left( {\Delta\; m^{i}} \right)}}\end{matrix}}$

Recall that τis the origin time that does not depend on x,y,z, but onlyon τ, and the travel time, T, is independent of the time of origin.

The double difference is defined by (Waldhauser & Ellsworth, 2000):dr _(k) ^(ij)=(t _(k) ^(i) −t _(k) ^(j))^(obs)−(t _(k) ^(i) −t _(k)^(j))^(cal)

Therefore, the following equation is the linearized double-difference ofarrival times:

${{\frac{\partial t_{k}^{i}}{\partial m}\Delta\; m^{i}} - {\frac{\partial t_{k}^{j}}{\partial m}\Delta\; m^{j}}} = {dr}_{k}^{ij}$where m is the x,y,z, and τ at the origin and t is the arrival time,based on a velocity model.

This can be written in matrix form, which for four events and onestation k is given in brief below. (The z and Δτ derivatives for fourevents have been deleted because of space constraints). There are 4Ncolumns (where N is the number of events) and M rows (where M is thenumber of event-pairs). It should be noted that seldom, if ever, do allstations record all of the events, so the size of the actual matrix maybe adjusted for each circumstance.

${\begin{pmatrix}\frac{\partial t_{k}^{1}}{\partial x} & {- \frac{\partial t_{k}^{2}}{\partial x}} & 0 & 0 & \frac{\partial t_{k}^{1}}{\partial y} & {- \frac{\partial t_{k}^{2}}{\partial y}} & 0 & 0 \\\frac{\partial t_{k}^{1}}{\partial x} & 0 & {- \frac{\partial t_{k}^{3}}{\partial x}} & 0 & \frac{\partial t_{k}^{1}}{\partial y} & 0 & {- \frac{\partial t_{k}^{3}}{\partial y}} & 0 \\\frac{\partial t_{k}^{1}}{\partial x} & 0 & 0 & \frac{\partial t_{k}^{4}}{\partial x} & \frac{\partial t_{k}^{1}}{\partial y} & 0 & 0 & {- \frac{\partial t_{k}^{4}}{\partial y}} \\0 & \frac{\partial t_{k}^{2}}{\partial x} & {- \frac{\partial t_{k}^{3}}{\partial x}} & 0 & 0 & \frac{\partial t_{k}^{2}}{\partial y} & {- \frac{\partial t_{k}^{3}}{\partial y}} & 0 \\0 & \frac{\partial t_{k}^{2}}{\partial x} & 0 & \frac{\partial t_{k}^{4}}{\partial x} & 0 & \frac{\partial t_{k}^{2}}{\partial y} & 0 & {- \frac{\partial t_{k}^{4}}{\partial y}} \\0 & 0 & \frac{\partial t_{k}^{3}}{\partial x} & {- \frac{\partial t_{k}^{4}}{\partial x}} & 0 & 0 & \frac{\partial t_{k}^{3}}{\partial y} & {- \frac{\partial t_{k}^{4}}{\partial y}}\end{pmatrix}\begin{bmatrix}{\Delta\; x_{1}} \\{\Delta\; x_{2}} \\{\Delta\; x_{3}} \\{\Delta\; x_{4}} \\{\Delta\; x_{1}} \\{\Delta\; x_{2}} \\{\Delta\; x_{3}} \\{\Delta\; x_{4}}\end{bmatrix}}\frac{\partial t_{k}^{1}}{\partial x}$type derivatives are the changes in travel times with change in x, y, zand are calculated numerically.

Labeling the matrix G, the vector Δm, and the double differences dr(m),then the equation to solve form is:G(Δm)=dr(m)

The solution for Δm that minimizes the square of the residual,(GΔm−dr(m))² is found through standard inverse techniques. Symbolically,the solution for Δm is given by:Δm=(G ^(T) G)⁻¹ G ^(T) dr(m)

The new values for m_(s+1)=m_(s)+Δm and is iterated until the change inthe residual is below a fixed tolerance (tol=1e-6 seconds). The doubledifference not only gives relative positions, but has been shown toyield absolute positions if noise is limited.

The inversion technique has several practical difficulties. The first ofthese is that the resulting matrix is sparse. In each row, only 8 of the4N elements (where N is the number of events) are non-zero. This canlead to instabilities in the solutions. The stability of the result canbe enhanced by using a routine by Page and Saunders (1982) for sparselinear equations and sparse least squares. This routine regularizes thematrix by using a damping factor, whose value is determined by acompromise between speed and accuracy. A damping factor of zero wouldtake full steps between iterations, while a damping of nearly one takesvery much smaller steps. A damping of 10⁻⁶ proves to be a reasonablecompromise.

The size of the matrix also can be problematic depending on the numberof events and stations. In many cases, stations do not receive allphases of the events and so rows of zeros appear. To reduce the size ofthe matrix and to eliminate unnecessary rows, a separate matrix can beused to track of which station receives which event. Thedouble-difference matrix is then collapsed to include only needed rows.

The double difference technique reliably gives only relative positionsof the events. This occurs since only differences of arrival times areused. Preferably, orienting the cluster once it is determined can bedone through the use of known events or by using an absolute locationroutine to identify the most certain event locations, in terms ofuncertainty ellipsoids, as anchor points.

To that end, the data processing system (400) can have software codethat starts from the raw traces, picks the microseismic events, usessemblance to “cluster” similar events and to refine the pick times, usesthe “single difference” locator described above to give initiallocations, and finally collapses those positions onto fracture linesusing a double-difference procedure.

h. Double-Difference Velocity Model Tomography

The system's processing can determine the event location and magnitudeusing double-difference velocity model tomography. The determination ofevent location and magnitude depends upon an accurate velocity model.There are a large number of techniques for determining such a velocitymodel, but a method based on the double-difference technique can be usedas detailed herein. In general, the objective is to minimize thedouble-difference in travel times with respect to the event location aswell as parameters describing the velocity model. The result preferablygives more precise locations with a refined velocity model.

The algorithm resembles the double-difference calculation above withadditional columns including differentiation with respect to velocitymodel parameters. This calculation requires a number of stations greaterthan the number of parameters being determined. The derivatives withrespect to the velocity model parameters need to be determinednumerically. This calculation does not lend itself to a grid system so atechnique that allows the calculation of travel times for smallvariations in velocity model parameters is preferred. A techniquecalculating differential travel times can use known event locations as“anchors” for preliminary velocity model refinement. These can beinitially used in the “single difference” location routine. In thiscase, the only variation will be for the velocity model parameters, notthe location.

D. Buried Array Sensor Details

As evidenced above in FIG. 11B, the sensors 152 in the buried array 150of disposed in shallow boreholes are subject to surface noise. As alsoevidenced throughout the present disclosure, the sensors 152 seek tomeasure weak signals of microseismic events. This can make sensing withthe buried sensors 152 difficult. Naturally, a more sensitive sensor 152used downhole would be expected to help solve the problem of detectingthe weak signals. Yet, the weak signal detected by a more sensitivesensor would be subjected to all of the detrimental effects of the noiseoccurring near the surface so that a more sensitive sensor would simplyexacerbate the problem. Due to these difficulties, the disclosed system100 uses a sensitive sensor that has a low noise floor as previouslydetailed.

FIG. 15A reproduces components of the disclosed system 100 previouslyidentified throughout the present disclosure. As shown, a buried array150 disposed in a borehole 154 has a plurality of seismic sensors 152.These sensors 152 can be geophones or the like disposed on a tubular 156and cemented in the borehole 154, or the sensors 152 can be hydrophonesor the like suspended in fluid in the borehole 154. As detailed herein,the sensor 152 can include a single-component geophone having a geophoneelement and having a low-noise amplifier integrated therein. Morepreferably, each sensor 152 installed in the borehole 154 as in FIG. 15Apreferably has at least three of the single-component geophones arrangedin three orthogonal axis at a vertical location in the borehole 154.

In either case, the sensors 152 are arranged relative to a subsurfacevolume and are coupled to the volume. Transmission lines 142 connect thedownhole sensors 152 to the recording unit 140 at the surface.Additionally, the recording unit 140 has a digitizer 146 that digitizesanalog signals from the sensors 152 and has storage 148 for storing thedigitized signals.

During operation, the seismic sensors 152 disposed downhole in theburied array 150 and connected to the recording unit 140 at the surfacewith transmission lines 142 respond to seismic energy (not shown) fromthe subsurface volume. As noted previously, for example, the sensors 152respond to the moveout of a microsiesmic event propagated from afracture, fault, or the like occurring in the subsurface volume.

In response to the detected energy, the sensor 152 generates an inputanalog signal, which can be very weak as noted herein. Because theenergy is weak, the sensor 152 used is preferably sensitive. Forexample, the sensor 152 can be sensitive to about 2 nm/s over most ofthe bandwidth of interest, but the sensitivity can vary and is dependenton frequency.

However, simply putting a more sensitive sensor downhole inherentlycreates more impedance and more internal noise. As expected, forexample, the sensor 152 has some intrinsic noise N_(D) that is added tothe input analog signal. For these reasons, the sensor 152 is not onlysensitive, but is also preferably has a low noise floor to better detectthe weak seismic energy under the surrounding conditions. Details of thelow noise floor of the sensor 152 have been described previously withrespect to FIG. 14. In one particular implementation, the noise floor ofthe sensor 152 is preferably as low as or below the ambient earth noisein the area of interest. The ambient earth noise can be characterized byPeterson's and USGS' New Low-Noise Model (NLNM), which can summarize thelowest observed vertical seismic noise levels through a seismicfrequency band.

In addition to being sensitive and having a low noise floor, the sensor152 directly amplifies the input analog signal by a downhole gain valueG_(D). In general, the downhole gain value G_(D) can depend on thecharacteristics of the implementation. In one embodiment, the sensor 152amplifies the input signal by a factor of 64, although other values canbe used. For example, a preferred high sensitivity geophone noted hereinhas a built-in amplifier inside the geophone by having the amplifierelectronics contained in the geophone's housing. As will be shown below,the amplified signal masks (i.e., lessens) any impact of the noisegained in the transmission line on the overall amplified analog signal.

The sensor 152 then sends the amplified analog signal to the upholerecording unit 140 via the transmission line 142. As is customary anddespite any efforts to mitigate the issue, the transmission line 142 issubject to electromagnetic interference, which adds noise N_(L) to thetransmitted signal. For example, as the propagated signal travels alongthe transmission line 142 from the sensor 152 to the recording unit 140at the surface, the signal acquires additional noise N_(L), such aselectromagnetic interference. It is desirable though to propagate thesignal without detrimentally affecting it. However, because the originalinput signal has been amplified directly at the sensor 152 downhole, anyadded noise N_(L) from the transmission line 142 is expected to havelittle effect in terms of scale compared to the amplified sensor signal.

At the surface, the recording unit 140 receives the transmittedamplified signal and adds its own uphole gain G_(U) and noise N_(U) tothe signal. The uphole gain value G_(U) and noise N_(U) is typicallyspecific to the recording unit 140 used and its electronic configurationand can be about any desirable value. After receiving and amplifying thesignal uphole, the recording unit 140 digitizes the amplified analogsignal sent with a digitizer 144 and stores the digitized signal instorage 146.

FIG. 15B shows the system 100 in more detail to discuss the gain andnoise levels involved. The downhole sensor 152 has at least onetransducer 600 with a compartmentalized design incorporating the sensorelectronics in the housing to minimize noise pick-up. Details related tothe preferred sensor 152 can be found in U.S. Pat. No. 7,518,954, whichis incorporated herein by reference in its entirety. During sensing,earth particle velocity is imparted to the transducer's housing andmagnet, and earth motion and Brownian noise are imparted to thetransducer's coil (602). In the sensor 152, the sum of the earth motionand Brownian noise is multiplied by the sensor's transduction (604).Then, the transducer's output is attenuated by voltage divider action(606), and Johnson noise is added on a RMS basis.

The amplifier (608) of the sensor 152 then increases the output signalby a gain G_(D), which in one embodiment is ×2⁶ (i.e., ×x64). Theamplifier (608) adds buffer and geophone noise (a) in terms of nV/√Hz.From the amplifier (608), the output signal is transmitted by thetransmission line 142, such as a downhole cable, to the surfacerecording unit 140. The line 142 has resistance that adds resistor noise(b) in terms of nV/rtHz to the output signal.

At the surface, the recording unit's amplifier (145) then increases theoutput signal by a gain G_(U), which in one embodiment is ×2⁰.Additionally, the amplifier adds buffer noise (c) in terms of nV/√Hz.Finally, an A/D converter 146 of the recording unit 140 converts theanalog signal to digital for eventual storage. The A/D converter 146adds A/D semiconductor noise (d) in terms of nV/√Hz.

As the system 100 in FIG. 15A shows, the sources of noise in the system100 of the sensor 152 and recording unit 140 creates an overall systemnoise value that can be calculated as:

${System\_ Noise} = \sqrt{a^{2} + \frac{b^{2} + c^{2} + d^{2}}{G_{D}^{2}}}$

As this equation indicated, the gain G_(D) (e.g., ×64) of the sensor'sbuffer makes all following noises occurring in the system 100practically negligible. (This equation assumes that the amplificationfactor G_(U) of the recording unit 140 is one (e.g., ×2⁰). If theamplification factor G_(U) is greater, the above equation would be moreinvolved. Nevertheless, a higher amplification factor G_(U) would justfurther mask any noise level N_(U) added by the unit 140.) Thus, therecording unit 140 at the surface need not necessarily have a low noisefloor. Accordingly, as shown previously in FIG. 12A, the noise floorassociated with the sensor 152 (e.g., 2.12 nm/s) essentially dominatesthe system 100.

In addition to having a sensitive sensor 152 with low-noise floor and inaddition to amplifying the analog signal at the sensor 152 beforetransmission, the disclosed system 100 preferably has furtherconsiderations made to the components of the system. As notedpreviously, the recording unit 140 chosen for use with the downholesensor 152 is preferably selected with a gain value G_(U) and noiselevel N_(U) to match the gain value G_(D) and noise level N_(D) of thesensor 152. This balance seeks to adjust the gain of the system 100while minimizing the effect of the noise generated from the transmissionline 142 so the system 100 can still detect low signals.

Either the uphole gain value GU, the downhole gain value GD, or both canbe selected to achieve the desired balance. For example, the system 100can be configured with a selected gain GD needed downhole at the sensor152 to properly balance the gain GU and noise NU of the recording unit140 and noise NL of the transmission line 142. Such a situation has beenshown previously in FIG. 12B, for example, which shows how a particulardownhole sensor (e.g., SM64 geophone noted herein) with downhole gainand noise values can be selected to improve the sensitivity and lownoise with respect to a number of recording units.

In another example, the desired balance between the downhole gain GD andthe uphole gain GU can involve selecting a value of the uphole gain GUrelative to the downhole gain GD that reduces the overall effect ofnoise in the system 100. For example, FIG. 12C discussed previouslyshows how particular uphole recording units with internal uphole gainand noise values can be selected to improve the sensitivity and lownoise with respect to a particular downhole sensor (e.g., SM-6 geophone)by reducing the overall effect of noise in the system 100.

E. Concluding Remarks

As will be appreciated, teachings of the present disclosure can beimplemented in digital electronic circuitry, computer hardware, computerfirmware, computer software, or any combination thereof. Teachings ofthe present disclosure can be implemented in a program storage device orcomputer program product tangibly embodied in a machine-readable storagedevice for execution by a programmable processor or programmable controldevice so that the programmable control device executing programinstructions can perform functions of the present disclosure. Theteachings of the present disclosure can be implemented advantageously inone or more computer programs that are executable on a programmablesystem (e.g., data processing system or the like) including at least oneprogrammable processor coupled to receive data and instructions from,and to transmit data and instructions to, a data storage system, atleast one input device, and at least one output device. Storage devicessuitable for tangibly embodying computer program instructions and datainclude all forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as EPROM, EEPROM, and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM disks. Any of the foregoing can besupplemented by, or incorporated in, ASICs (application-specificintegrated circuits).

The foregoing description of preferred and other embodiments is notintended to limit or restrict the scope or applicability of theinventive concepts conceived of by the Applicants. It will beappreciated with the benefit of the present disclosure that featuresdescribed above in accordance with any embodiment or aspect of thedisclosed subject matter can be utilized, either alone or incombination, with any other described feature, in any other embodimentor aspect of the disclosed subject matter.

In exchange for disclosing the inventive concepts contained herein, theApplicants desire all patent rights afforded by the appended claims.Therefore, it is intended that the appended claims include allmodifications and alterations to the full extent that they come withinthe scope of the following claims or the equivalents thereof.

What is claimed is:
 1. A seismic surveying method of a subsurfacevolume, the method comprising: disposing at least one seismic sensor ofa system downhole, the at least one seismic sensor having a downholeamplifier with a downhole gain to directly amplify an input analogsignal generated at the at least one sensor; generating the input analogsignal with the at least one sensor in response to seismic energy fromthe subsurface volume; amplifying the input analog signal directly atthe at least one sensor by the downhole gain of the downhole amplifier;sending the amplified analog signal to an uphole recording unit of thesystem via a transmission line, the uphole recording unit having anuphole amplifier with an uphole gain to directly amplify the amplifiedanalog signal received at the uphole recording unit; amplifying theamplified analog signal directly at the uphole recording unit by theuphole gain of the uphole amplifier; and reducing an overall effect ofnoise in the system by selecting the downhole gain of the downholeamplifier and the uphole gain of the uphole amplifier to be balancedwith respect to one another.
 2. The method of claim 1, wherein thetransmission line has a noise level adding to the amplified analogsignal, and wherein amplifying the input analog signal directly at theat least one sensor by the downhole gain comprises masking the addednoise level from the transmission line.
 3. The method of claim 1,wherein the uphole recording unit has a noise level adding to theamplified analog signal, and wherein amplifying the input analog signaldirectly at the at least one sensor by the downhole gain comprisesmasking the added noise level from the recording unit.
 4. The method ofclaim 1, wherein selecting the downhole gain and the uphole gain to bebalanced with respect to one another comprises selecting a value of theuphole gain relative to the downhole gain that reduces the overalleffect of noise.
 5. The method of claim 1, wherein selecting thedownhole gain and the uphole gain to be balanced with respect to oneanother comprises selecting a value of the downhole gain relative to theuphole gain that reduces the overall effect of noise.
 6. The method ofclaim 1, further comprising: digitizing the amplified analog signal sentto the uphole recording unit; and storing the digitized signal.
 7. Themethod of claim 1, wherein the at least one sensor comprises a lowintrinsic noise floor relative to the seismic energy of the subsurfacevolume.
 8. The method of claim 7, wherein the low intrinsic noise flooris at least as low as ambient earth noise associated with the subsurfacevolume.
 9. The method of claim 1, wherein the at least one sensorcomprises an accelerometer, a geophone, a hydrophone, a fiber opticsensor, or a microphone.
 10. The method of claim 1, wherein the at leastone sensor comprises a single-component geophone having a geophoneelement and having the downhole amplifier integrated therein.
 11. Themethod of claim 10, wherein the at least one sensor comprises at leastthree of the single-component geophones disposed orthogonal to oneanother at a same location downhole.
 12. The method of claim 1, whereindisposing the at least one seismic sensor downhole comprises couplingthe at least one sensor in a borehole disposed in the subsurface volume.13. The method of claim 12, wherein coupling the at least one sensor inthe borehole disposed in the subsurface volume comprises: drilling theborehole into the subsurface volume; disposing the at least one sensoron a tubular; disposing the tubular in the borehole; and coupling the atleast one sensor to the subsurface volume adjacent the borehole.
 14. Themethod of claim 13, wherein coupling the at least one sensor to thesubsurface volume adjacent the borehole comprises cementing the at leastone sensor in the borehole or suspending the at least one sensor influid in the borehole.
 15. A system for seismic surveying a subsurfacevolume, the system comprising: at least one seismic sensor disposeddownhole, the at least one seismic sensor having a downhole amplifierwith a downhole gain to directly amplify an input analog signalgenerated at the at least one sensor, the at least one seismic sensorgenerating the input analog signal in response to seismic energy fromthe subsurface volume, the downhole amplifier of the at least oneseismic sensor directly amplifying the input analog signal by thedownhole gain and transmitting the amplified analog signal; and anuphole recording unit connected to the at least one seismic sensor via atransmission line and receiving the amplified analog signal, the upholerecording unit having an uphole amplifier with an uphole gain todirectly amplify the amplified analog signal received at the upholerecording unit, the uphole recording unit amplifying the amplifiedanalog signal directly at the uphole recording unit by the uphole gain,wherein the downhole gain of the downhole amplifier and the uphole gainof the uphole amplifier are selected to be balanced with respect to oneanother to reduce an overall effect of noise in the system.
 16. Thesystem of claim 15, wherein the transmission line has a noise leveladding to the amplified analog signal, and wherein the downhole gainamplifying the input analog signal masks the added noise level from thetransmission line.
 17. The system of claim 15, wherein a value of theuphole gain is selected relative to the downhole gain to reduce theoverall effect of noise in the system.
 18. The system of claim 15,wherein a value of the downhole gain is selected relative to the upholegain to reduce the overall effect of noise in the system.
 19. The systemof claim 15, wherein the recording unit comprises: a digitizerdigitizing the amplified analog signal; and storage storing thedigitized signal.
 20. The system of claim 15, wherein the at least onesensor comprises a low intrinsic noise floor relative to the seismicenergy of the subsurface volume.
 21. The system of claim 20, wherein thelow intrinsic noise floor is at least as low as ambient earth noiseassociated with the subsurface volume.
 22. The system of claim 15,wherein the at least one sensor comprises an accelerometer, a geophone,a hydrophone, a fiber optic sensor, or a microphone.
 23. The system ofclaim 15, wherein the at least one sensor comprises a single-componentgeophone having a geophone element; and wherein the downhole amplifiercomprises a low-noise amplifier integrated in the single-componentgeophone.
 24. The system of claim 23, wherein the at least one sensorcomprises at least three of the single-component geophones disposedorthogonal to one another at a same location downhole.
 25. The system ofclaim 15, wherein the at least one seismic sensor comprises a tubular onwhich the sensor disposed, the tubular disposed in the borehole, the atleast one sensor coupled to the subsurface volume adjacent the borehole.26. The system of claim 25, wherein cementation or fluid couples the atleast one sensor in the borehole.